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100644 third_party/Matcha-TTS/scripts/schedule.sh create mode 100644 third_party/Matcha-TTS/setup.py create mode 100644 third_party/Matcha-TTS/synthesis.ipynb create mode 100644 tools/extract_embedding.py create mode 100644 tools/extract_speech_token.py create mode 100644 tools/make_parquet_list.py create mode 100644 webui.py diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..dd84ea7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,38 @@ +--- +name: Bug report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1. Go to '...' +2. Click on '....' +3. Scroll down to '....' +4. See error + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. iOS] + - Browser [e.g. chrome, safari] + - Version [e.g. 22] + +**Smartphone (please complete the following information):** + - Device: [e.g. iPhone6] + - OS: [e.g. iOS8.1] + - Browser [e.g. stock browser, safari] + - Version [e.g. 22] + +**Additional context** +Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000..bbcbbe7 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: '' +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context or screenshots about the feature request here. diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml new file mode 100644 index 0000000..5dc2204 --- /dev/null +++ b/.github/workflows/lint.yml @@ -0,0 +1,56 @@ +name: Lint + +on: + pull_request: + push: + +jobs: + quick-checks: + runs-on: ubuntu-latest + steps: + - name: Fetch CosyVoice + uses: actions/checkout@v1 + - name: Checkout PR tip + run: | + set -eux + if [[ "${{ github.event_name }}" == "pull_request" ]]; then + # We are on a PR, so actions/checkout leaves us on a merge commit. + # Check out the actual tip of the branch. + git checkout ${{ github.event.pull_request.head.sha }} + fi + echo ::set-output name=commit_sha::$(git rev-parse HEAD) + id: get_pr_tip + - name: Ensure no tabs + run: | + (! git grep -I -l $'\t' -- . ':(exclude)*.txt' ':(exclude)*.svg' ':(exclude)**Makefile' ':(exclude)**/contrib/**' ':(exclude)third_party' ':(exclude).gitattributes' ':(exclude).gitmodules' || (echo "The above files have tabs; please convert them to spaces"; false)) + - name: Ensure no trailing whitespace + run: | + (! git grep -I -n $' $' -- . ':(exclude)*.txt' ':(exclude)third_party' ':(exclude).gitattributes' ':(exclude).gitmodules' || (echo "The above files have trailing whitespace; please remove them"; false)) + + flake8-py3: + runs-on: ubuntu-latest + steps: + - name: Setup Python + uses: actions/setup-python@v1 + with: + python-version: 3.9 + architecture: x64 + - name: Fetch CosyVoice + uses: actions/checkout@v1 + - name: Checkout PR tip + run: | + set -eux + if [[ "${{ github.event_name }}" == "pull_request" ]]; then + # We are on a PR, so actions/checkout leaves us on a merge commit. + # Check out the actual tip of the branch. + git checkout ${{ github.event.pull_request.head.sha }} + fi + echo ::set-output name=commit_sha::$(git rev-parse HEAD) + id: get_pr_tip + - name: Run flake8 + run: | + set -eux + pip install flake8==3.8.2 flake8-bugbear flake8-comprehensions flake8-executable flake8-pyi==20.5.0 mccabe pycodestyle==2.6.0 pyflakes==2.2.0 + flake8 --version + flake8 --max-line-length 150 --ignore B006,B008,B905,C408,E402,E731,E741,W503,W504 --exclude ./third_party/,./runtime/python/grpc/cosyvoice_pb2*py + if [ $? != 0 ]; then exit 1; fi \ No newline at end of file diff --git a/.github/workflows/stale-issues.yml b/.github/workflows/stale-issues.yml new file mode 100644 index 0000000..d5cd3cf --- /dev/null +++ b/.github/workflows/stale-issues.yml @@ -0,0 +1,22 @@ +name: Close inactive issues +on: + schedule: + - cron: "30 1 * * *" + +jobs: + close-issues: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/stale@v5 + with: + days-before-issue-stale: 30 + days-before-issue-close: 14 + stale-issue-label: "stale" + stale-issue-message: "This issue is stale because it has been open for 30 days with no activity." + close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale." + days-before-pr-stale: -1 + days-before-pr-close: -1 + repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..d135654 --- /dev/null +++ b/.gitignore @@ -0,0 +1,52 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class +.venv/ + +# Visual Studio Code files +.vscode +.vs + +# PyCharm files +.idea + +# Eclipse Project settings +*.*project +.settings + +# Sublime Text settings +*.sublime-workspace +*.sublime-project + +# Editor temporaries +*.swn +*.swo +*.swp +*.swm +*~ + +# IPython notebook checkpoints +.ipynb_checkpoints + +# macOS dir files +.DS_Store + +exp +data +raw_wav +tensorboard +**/*build* + +# Clangd files +.cache +compile_commands.json + +# train/inference files +*.wav +*.m4a +*.aac +*.pt +pretrained_models/* +*_pb2_grpc.py +*_pb2.py \ No newline at end of file diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000..3d1b157 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "third_party/Matcha-TTS"] + path = third_party/Matcha-TTS + url = https://github.com/shivammehta25/Matcha-TTS.git diff --git a/0_Inference_QWen2.5.py b/0_Inference_QWen2.5.py new file mode 100644 index 0000000..972a811 --- /dev/null +++ b/0_Inference_QWen2.5.py @@ -0,0 +1,73 @@ +from transformers import AutoModelForCausalLM, AutoTokenizer + +from cosyvoice.cli.cosyvoice import CosyVoice +from cosyvoice.utils.file_utils import load_wav +import torchaudio + +import pygame +import time + +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +model_name = r"D:\AI\download\Qwen2.5-0.5B-Instruct" +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +cosyvoice = CosyVoice(r'../pretrained_models/CosyVoice-300M', load_jit=True, load_onnx=False, fp16=True) +# sft usage +print(cosyvoice.list_avaliable_spks()) + +prompt = "你好，你叫什么名字?" +messages = [ + {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, + {"role": "user", "content": prompt}, +] +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, +) +model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + +generated_ids = model.generate( + **model_inputs, + max_new_tokens=512, +) +generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) +] + +response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + +print("Input:", prompt) +print("Answer:", response) + +# ['中文女', '中文男', '日语男', '粤语女', '英文女', '英文男', '韩语女'] +for i, j in enumerate(cosyvoice.inference_sft(f'{prompt}', '中文男', stream=False)): + torchaudio.save('prompt_sft_{}.wav'.format(i), j['tts_speech'], 22050) + # play_audio('prompt_sft_{}.wav'.format(i)) + +# change stream=True for chunk stream inference +for i, j in enumerate(cosyvoice.inference_sft(f'{response}', '中文女', stream=False)): + torchaudio.save('sft_{}.wav'.format(i), j['tts_speech'], 22050) + # play_audio('sft_{}.wav'.format(i)) + +play_audio('prompt_sft_0.wav') +for i, j in enumerate(f'{response}'): + play_audio('sft_{}.wav'.format(i)) + diff --git a/10_SenceVoice_QWen2.5_cosyVoice.py b/10_SenceVoice_QWen2.5_cosyVoice.py new file mode 100644 index 0000000..5509769 --- /dev/null +++ b/10_SenceVoice_QWen2.5_cosyVoice.py @@ -0,0 +1,156 @@ +from transformers import AutoModelForCausalLM, AutoTokenizer +from cosyvoice.cli.cosyvoice import CosyVoice +from cosyvoice.utils.file_utils import load_wav +from funasr import AutoModel +import torchaudio +import pygame +import time +import sys +import sounddevice as sd +from scipy.io.wavfile import write +import numpy as np + +def record_audio(filename="output.wav", sample_rate=44100): + print("按下 Enter 开始录音...") + input() # 等待用户按下 Enter 键开始录音 + print("录音中... 按下 Enter 键结束录音") + + # 开始录音 + recording = [] + try: + def callback(indata, frames, time, status): + recording.append(indata.copy()) + with sd.InputStream(samplerate=sample_rate, channels=1, callback=callback): + input() # 等待用户再次按下 Enter 键结束录音 + except Exception as e: + print(f"录音出现错误: {e}") + return + + # 将录音数据合并并保存为 WAV 文件 + audio_data = np.concatenate(recording, axis=0) + write(filename, sample_rate, (audio_data * 32767).astype(np.int16)) + print(f"录音已保存为 {filename}") + + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +import os +import shutil + +def clear_folder(folder_path): + # 检查文件夹是否存在 + if not os.path.exists(folder_path): + os.makedirs(folder_path, exist_ok=True) + print(f"文件夹 '{folder_path}' 不存在，已创建") + return + + # 获取文件夹中的所有文件和子文件夹 + items = os.listdir(folder_path) + + # 如果文件夹为空，直接返回 + if not items: + print(f"文件夹 '{folder_path}' 已经为空") + return + + # 遍历文件和文件夹并删除 + for item in items: + item_path = os.path.join(folder_path, item) + + # 判断是否是文件夹或文件 + if os.path.isfile(item_path): + os.remove(item_path) # 删除文件 + print(f"删除文件: {item_path}") + elif os.path.isdir(item_path): + shutil.rmtree(item_path) # 删除文件夹及其内容 + print(f"删除文件夹: {item_path}") + + print(f"文件夹 '{folder_path}' 已清空") + +# ------------------- 模型初始化 --------------- +# --- SenceVoice-语音识别模型 +model_dir = r"D:\AI\download\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) + +# --- QWen2.5大语言模型 --- +# model_name = r":\2_PYTHON\Project\GPT\QWen\Qwen2.5-0.5B-Instruct" +model_name = r"D:\AI\download\Qwen2.5-1.5B-Instruct" +# model_name = r':\2_PYTHON\Project\GPT\QWen\Qwen2.5-7B-Instruct-GPTQ-Int4' +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +# --- CosyVoice - 语音合成模型 +cosyvoice = CosyVoice(r'D:\AI\download\CosyVoice-300M', load_jit=True, load_onnx=False, fp16=True) +# --- CosyVoice - 支持的音色列表 +print(cosyvoice.list_avaliable_spks()) +# ------------------ 模型初始化结束 ---------------- + +while(1): + # 使用函数录音，作为输入 + record_audio("my_recording.wav") + + # input_file = ( "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" ) + input_file = ("my_recording.wav") + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + + # -------- 模型推理阶段，将语音识别结果作为大模型Prompt ------ + prompt = res[0]['text'].split(">")[-1] + "，回答简短一些，保持50字以内！" + messages = [ + {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, + {"role": "user", "content": prompt}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + + generated_ids = model.generate( + **model_inputs, + max_new_tokens=512, + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + + print("Input:", prompt) + print("Answer:", response) + + # --- 答复输出文件夹 --- + folder_path = "./out_answer/" + clear_folder(folder_path) + + # ['中文女', '中文男', '日语男', '粤语女', '英文女', '英文男', '韩语女'] + # change stream=True for chunk stream inference + index_out = 0 + for i, j in enumerate(cosyvoice.inference_sft(f'{response}', '中文女', stream=False)): + torchaudio.save('{}/sft_{}.wav'.format(folder_path,i), j['tts_speech'], 22050) + index_out += 1 + # play_audio('sft_{}.wav'.format(i)) + + for idx in range(index_out): + play_audio('{}/sft_{}.wav'.format(folder_path,idx)) + diff --git a/11_SenceVoice_QWen2.5_pytts3.py b/11_SenceVoice_QWen2.5_pytts3.py new file mode 100644 index 0000000..d3011e2 --- /dev/null +++ b/11_SenceVoice_QWen2.5_pytts3.py @@ -0,0 +1,158 @@ +from transformers import AutoModelForCausalLM, AutoTokenizer +from cosyvoice.cli.cosyvoice import CosyVoice +from cosyvoice.utils.file_utils import load_wav +from funasr import AutoModel +import torchaudio +import pygame +import time +import sys +import sounddevice as sd +from scipy.io.wavfile import write +import numpy as np + +def record_audio(filename="output.wav", sample_rate=44100): + print("按下 Enter 开始录音...") + input() # 等待用户按下 Enter 键开始录音 + print("录音中... 按下 Enter 键结束录音") + + # 开始录音 + recording = [] + try: + def callback(indata, frames, time, status): + recording.append(indata.copy()) + with sd.InputStream(samplerate=sample_rate, channels=1, callback=callback): + input() # 等待用户再次按下 Enter 键结束录音 + except Exception as e: + print(f"录音出现错误: {e}") + return + + # 将录音数据合并并保存为 WAV 文件 + audio_data = np.concatenate(recording, axis=0) + write(filename, sample_rate, (audio_data * 32767).astype(np.int16)) + print(f"录音已保存为 {filename}") + + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +import os +import shutil + +def clear_folder(folder_path): + # 检查文件夹是否存在 + if not os.path.exists(folder_path): + os.makedirs(folder_path, exist_ok=True) + print(f"文件夹 '{folder_path}' 不存在，已创建") + return + + # 获取文件夹中的所有文件和子文件夹 + items = os.listdir(folder_path) + + # 如果文件夹为空，直接返回 + if not items: + print(f"文件夹 '{folder_path}' 已经为空") + return + + # 遍历文件和文件夹并删除 + for item in items: + item_path = os.path.join(folder_path, item) + + # 判断是否是文件夹或文件 + if os.path.isfile(item_path): + os.remove(item_path) # 删除文件 + print(f"删除文件: {item_path}") + elif os.path.isdir(item_path): + shutil.rmtree(item_path) # 删除文件夹及其内容 + print(f"删除文件夹: {item_path}") + + print(f"文件夹 '{folder_path}' 已清空") + +# ------------------- 模型初始化 --------------- +# --- SenceVoice-语音识别模型 +model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) + +# --- QWen2.5大语言模型 --- +# model_name = r":\2_PYTHON\Project\GPT\QWen\Qwen2.5-0.5B-Instruct" +model_name = r"E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-1.5B-Instruct" +# model_name = r':\2_PYTHON\Project\GPT\QWen\Qwen2.5-7B-Instruct-GPTQ-Int4' +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +import pyttsx3 +# 初始化 TTS 引擎 +engine = pyttsx3.init() +# 设置语音属性 +engine.setProperty('rate', 200) # 语速engine.setProperty('volume', 0.9) # 音量（0.0 到 1.0） +# 选择语音 +voices = engine.getProperty('voices') +# print(voices) +engine.setProperty('voice', voices[0].id) # 使用第一个语音 + +while(1): + # 使用函数录音，作为输入 + record_audio("my_recording.wav") + + # input_file = ( "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" ) + input_file = ("my_recording.wav") + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + + # -------- 模型推理阶段，将语音识别结果作为大模型Prompt ------ + prompt = res[0]['text'].split(">")[-1] + "，回答简短一些，保持50字以内！" + messages = [ + {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, + {"role": "user", "content": prompt}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + + generated_ids = model.generate( + **model_inputs, + max_new_tokens=512, + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + + print("Input:", prompt) + print("Answer:", response) + + # 答复输出文件夹 + folder_path = "./out_answer/" + clear_folder(folder_path) + + # 输入文本 + text = response + # 朗读文本 + # engine.say(text) + # # 等待朗读完成 + # engine.runAndWait() + engine.save_to_file(text, os.path.join(folder_path,"sft_0.wav")) + engine.runAndWait() + play_audio(f'{folder_path}/sft_0.wav') diff --git a/12_SenceVoice_QWen2.5_edgeTTS.py b/12_SenceVoice_QWen2.5_edgeTTS.py new file mode 100644 index 0000000..51bd575 --- /dev/null +++ b/12_SenceVoice_QWen2.5_edgeTTS.py @@ -0,0 +1,157 @@ +from transformers import AutoModelForCausalLM, AutoTokenizer +from cosyvoice.cli.cosyvoice import CosyVoice +from cosyvoice.utils.file_utils import load_wav +from funasr import AutoModel +import torchaudio +import pygame +import time +import sys +import sounddevice as sd +from scipy.io.wavfile import write +import numpy as np +import asyncio +import edge_tts +import os +import shutil + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + +def record_audio(filename="output.wav", sample_rate=44100): + print("按下 Enter 开始录音...") + input() # 等待用户按下 Enter 键开始录音 + print("录音中... 按下 Enter 键结束录音") + + # 开始录音 + recording = [] + try: + def callback(indata, frames, time, status): + recording.append(indata.copy()) + with sd.InputStream(samplerate=sample_rate, channels=1, callback=callback): + input() # 等待用户再次按下 Enter 键结束录音 + except Exception as e: + print(f"录音出现错误: {e}") + return + + # 将录音数据合并并保存为 WAV 文件 + audio_data = np.concatenate(recording, axis=0) + write(filename, sample_rate, (audio_data * 32767).astype(np.int16)) + print(f"录音已保存为 {filename}") + + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + + +def clear_folder(folder_path): + # 检查文件夹是否存在 + if not os.path.exists(folder_path): + os.makedirs(folder_path, exist_ok=True) + print(f"文件夹 '{folder_path}' 不存在，已创建") + return + + # 获取文件夹中的所有文件和子文件夹 + items = os.listdir(folder_path) + + # 如果文件夹为空，直接返回 + if not items: + print(f"文件夹 '{folder_path}' 已经为空") + return + + # 遍历文件和文件夹并删除 + for item in items: + item_path = os.path.join(folder_path, item) + + # 判断是否是文件夹或文件 + if os.path.isfile(item_path): + os.remove(item_path) # 删除文件 + print(f"删除文件: {item_path}") + elif os.path.isdir(item_path): + shutil.rmtree(item_path) # 删除文件夹及其内容 + print(f"删除文件夹: {item_path}") + + print(f"文件夹 '{folder_path}' 已清空") + +# ------------------- 模型初始化 --------------- +# --- SenceVoice-语音识别模型 +model_dir = r"D:\AI\download\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) + + +# --- QWen2.5大语言模型 --- +model_name = r"D:\AI\download\Qwen2.5-0.5B-Instruct" +# model_name = r"E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-1.5B-Instruct" +# model_name = r':\2_PYTHON\Project\GPT\QWen\Qwen2.5-7B-Instruct-GPTQ-Int4' +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +# --- CosyVoice - 语音合成模型 ------ +# cosyvoice = CosyVoice(r'../pretrained_models/CosyVoice-300M', load_jit=True, load_onnx=False, fp16=True) +# print(cosyvoice.list_avaliable_spks()) +# --- CosyVoice - 支持的音色列表 +# ------------------ 模型初始化结束 ---------------- + + +while(1): + # 使用函数录音，作为输入 + record_audio("my_recording.wav") + + input_file = ("my_recording.wav") + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + + # -------- 模型推理阶段，将语音识别结果作为大模型Prompt ------ + prompt = res[0]['text'].split(">")[-1] + "，回答简短一些，保持50字以内！" + messages = [ + {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, + {"role": "user", "content": prompt}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + + generated_ids = model.generate( + **model_inputs, + max_new_tokens=512, + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + + print("Input:", prompt) + print("Answer:", response) + + # 答复输出文件夹 + folder_path = "./out_answer/" + clear_folder(folder_path) + + # 输入文本 + text = response + asyncio.run(amain(text, "zh-CN-XiaoyiNeural", os.path.join(folder_path,"sft_0.mp3"))) + play_audio(f'{folder_path}/sft_0.mp3') \ No newline at end of file diff --git a/13_1_SenceVoice_QWen2.5_kokoro_realTime.py b/13_1_SenceVoice_QWen2.5_kokoro_realTime.py new file mode 100644 index 0000000..3aede53 --- /dev/null +++ b/13_1_SenceVoice_QWen2.5_kokoro_realTime.py @@ -0,0 +1,355 @@ +import cv2 +import pyaudio +import wave +import threading +import numpy as np +import time +from queue import Queue +import webrtcvad +import os +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from transformers import AutoModelForCausalLM, AutoTokenizer +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +# import edge_tts +from kokoro import KPipeline +import asyncio +from time import sleep +import langid +from langdetect import detect +from IPython.display import display, Audio +import soundfile as sf + +# kokoro 语音合成 +# 🇪🇸 'e' => Spanish es +# 🇫🇷 'f' => French fr-fr +# 🇮🇳 'h' => Hindi hi +# 🇮🇹 'i' => Italian it +# 🇧🇷 'p' => Brazilian Portuguese pt-br +# 🇺🇸 'a' => American English, 🇬🇧 'b' => British English +# 🇯🇵 'j' => Japanese: pip install misaki[ja] +# 🇨🇳 'z' => Mandarin Chinese: pip install misaki[zh] +root_voice = r'E:\2_PYTHON\Project\TTS\Kokoro-82M\voices' + +def tts_kokoro(text, outpath, lid='z', voice_glo='zm_yunjian'): + global root_voice + pipeline = KPipeline(lang_code=lid) + voice_tensor = torch.load(os.path.join(root_voice, voice_glo+'.pt'), weights_only=True) + generator = pipeline( + text, voice=voice_tensor, + speed=1, split_pattern=r'\n+' + ) + + for i, (gs, ps, audio) in enumerate(generator): + # display(Audio(data=audio, rate=24000, autoplay=i==0)) + sf.write(f'{outpath}', audio, 24000) # save each audio file + +# --- 配置huggingFace国内镜像 --- +import os +os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' + +# 参数设置 +AUDIO_RATE = 16000 # 音频采样率 +AUDIO_CHANNELS = 1 # 单声道 +CHUNK = 1024 # 音频块大小 +VAD_MODE = 3 # VAD 模式 (0-3, 数字越大越敏感) +OUTPUT_DIR = "./output" # 输出目录 +NO_SPEECH_THRESHOLD = 1 # 无效语音阈值，单位：秒 +folder_path = "./Test_QWen2_VL/" +audio_file_count = 0 + +# 确保输出目录存在 +os.makedirs(OUTPUT_DIR, exist_ok=True) +os.makedirs(folder_path, exist_ok=True) + +# 队列用于音频和视频同步缓存 +audio_queue = Queue() +video_queue = Queue() + +# 全局变量 +last_active_time = time.time() +recording_active = True +segments_to_save = [] +saved_intervals = [] +last_vad_end_time = 0 # 上次保存的 VAD 有效段结束时间 + +# 初始化 WebRTC VAD +vad = webrtcvad.Vad() +vad.set_mode(VAD_MODE) + +# 音频录制线程 +def audio_recorder(): + global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time + + p = pyaudio.PyAudio() + stream = p.open(format=pyaudio.paInt16, + channels=AUDIO_CHANNELS, + rate=AUDIO_RATE, + input=True, + frames_per_buffer=CHUNK) + + audio_buffer = [] + print("音频录制已开始") + + while recording_active: + data = stream.read(CHUNK) + audio_buffer.append(data) + + # 每 0.5 秒检测一次 VAD + if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5: + # 拼接音频数据并检测 VAD + raw_audio = b''.join(audio_buffer) + vad_result = check_vad_activity(raw_audio) + + if vad_result: + print("检测到语音活动") + last_active_time = time.time() + segments_to_save.append((raw_audio, time.time())) + else: + print("静音中...") + + audio_buffer = [] # 清空缓冲区 + + # 检查无效语音时间 + if time.time() - last_active_time > NO_SPEECH_THRESHOLD: + # 检查是否需要保存 + if segments_to_save and segments_to_save[-1][1] > last_vad_end_time: + save_audio_video() + last_active_time = time.time() + else: + pass + # print("无新增语音段，跳过保存") + + stream.stop_stream() + stream.close() + p.terminate() + +# 视频录制线程 +def video_recorder(): + global video_queue, recording_active + + cap = cv2.VideoCapture(0) # 使用默认摄像头 + print("视频录制已开始") + + while recording_active: + ret, frame = cap.read() + if ret: + video_queue.put((frame, time.time())) + + # 实时显示摄像头画面 + cv2.imshow("Real Camera", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 键退出 + break + else: + print("无法获取摄像头画面") + + cap.release() + cv2.destroyAllWindows() + +# 检测 VAD 活动 +def check_vad_activity(audio_data): + # 将音频数据分块检测 + num, rate = 0, 0.4 + step = int(AUDIO_RATE * 0.02) # 20ms 块大小 + flag_rate = round(rate * len(audio_data) // step) + + for i in range(0, len(audio_data), step): + chunk = audio_data[i:i + step] + if len(chunk) == step: + if vad.is_speech(chunk, sample_rate=AUDIO_RATE): + num += 1 + + if num > flag_rate: + return True + return False + +# 保存音频和视频 +def save_audio_video(): + pygame.mixer.init() + + global segments_to_save, video_queue, last_vad_end_time, saved_intervals + + # 全局变量，用于保存音频文件名计数 + global audio_file_count + audio_file_count += 1 + audio_output_path = f"{OUTPUT_DIR}/audio_{audio_file_count}.wav" + # audio_output_path = f"{OUTPUT_DIR}/audio_0.wav" + + if not segments_to_save: + return + + # 停止当前播放的音频 + if pygame.mixer.music.get_busy(): + pygame.mixer.music.stop() + print("检测到新的有效音，已停止当前音频播放") + + # 获取有效段的时间范围 + start_time = segments_to_save[0][1] + end_time = segments_to_save[-1][1] + + # 检查是否与之前的片段重叠 + if saved_intervals and saved_intervals[-1][1] >= start_time: + print("当前片段与之前片段重叠，跳过保存") + segments_to_save.clear() + return + + # 保存音频 + audio_frames = [seg[0] for seg in segments_to_save] + + wf = wave.open(audio_output_path, 'wb') + wf.setnchannels(AUDIO_CHANNELS) + wf.setsampwidth(2) # 16-bit PCM + wf.setframerate(AUDIO_RATE) + wf.writeframes(b''.join(audio_frames)) + wf.close() + print(f"音频保存至 {audio_output_path}") + + # Inference() + # 使用线程执行推理 + inference_thread = threading.Thread(target=Inference, args=(audio_output_path,)) + inference_thread.start() + + # 记录保存的区间 + saved_intervals.append((start_time, end_time)) + + # 清空缓冲区 + segments_to_save.clear() + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +# async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: +# """Main function""" +# communicate = edge_tts.Communicate(TEXT, VOICE) +# await communicate.save(OUTPUT_FILE) + + +# -------- SenceVoice 语音识别 --模型加载----- +model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) + +# --- QWen2.5大语言模型 --- +# model_name = r"E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-0.5B-Instruct" +model_name = r"E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-1.5B-Instruct" +# model_name = r'E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-7B-Instruct-GPTQ-Int4' +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +def Inference(TEMP_AUDIO_FILE=f"{OUTPUT_DIR}/audio_0.wav"): + # -------- SenceVoice 推理 --------- + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + # prompt = res[0]['text'].split(">")[-1] + prompt = res[0]['text'].split(">")[-1] + "，回答简短一些，保持50字以内！" + print("ASR OUT:", prompt) + # ---------SenceVoice --end---------- + # -------- 模型推理阶段，将语音识别结果作为大模型Prompt ------ + messages = [ + {"role": "system", "content": "你叫千问，是一个18岁的女大学生，性格活泼开朗，说话俏皮"}, + {"role": "user", "content": prompt}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + + generated_ids = model.generate( + **model_inputs, + max_new_tokens=512, + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + output_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + + print("answer", output_text) + + # 输入文本 + text = output_text + # 语种识别 -- langid + language, confidence = langid.classify(text) + # 语种识别 -- langdetect + # language = detect(text).split("-")[0] + + # 🇪🇸 'e' => Spanish es + # 🇫🇷 'f' => French fr-fr + # 🇮🇳 'h' => Hindi hi + # 🇮🇹 'i' => Italian it + # 🇧🇷 'p' => Brazilian Portuguese pt-br + # 🇺🇸 'a' => American English, 🇬🇧 'b' => British English + # 🇯🇵 'j' => Japanese: pip install misaki[ja] + # 🇨🇳 'z' => Mandarin Chinese: pip install misaki[zh] + + language_speaker = { + "ja" : "j", # ok + "fr" : "f", # ok + "es" : "e", # ok + "zh" : "z", # ok + "en" : "a", # ok + } + + language_spk = { + "j" : "jf_nezumi", # ok + "f" : "ff_siwis", # ok + "e" : "em_santa", # ok + "z" : "zm_yunyang", # ok + "a" : "af_heart", # ok + } + + if language not in language_speaker.keys(): + used_speaker = "z" + else: + used_speaker = language_speaker[language] + print("检测到语种：", language, "使用音色：", language_speaker[language]) + + global audio_file_count + outpath = os.path.join(folder_path,f"sft_{audio_file_count}.wav") + tts_kokoro(text, outpath, lid=used_speaker, voice_glo=language_spk[used_speaker]) + play_audio(f'{folder_path}/sft_{audio_file_count}.wav') + +# 主函数 +if __name__ == "__main__": + + try: + # 启动音视频录制线程 + audio_thread = threading.Thread(target=audio_recorder) + # video_thread = threading.Thread(target=video_recorder) + audio_thread.start() + # video_thread.start() + + print("按 Ctrl+C 停止录制") + while True: + time.sleep(1) + + except KeyboardInterrupt: + print("录制停止中...") + recording_active = False + audio_thread.join() + # video_thread.join() + print("录制已停止") diff --git a/13_SenceVoice_QWen2.5_edgeTTS_realTime.py b/13_SenceVoice_QWen2.5_edgeTTS_realTime.py new file mode 100644 index 0000000..7ea53d3 --- /dev/null +++ b/13_SenceVoice_QWen2.5_edgeTTS_realTime.py @@ -0,0 +1,311 @@ +import cv2 +import pyaudio +import wave +import threading +import numpy as np +import time +from queue import Queue +import webrtcvad +import os +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from transformers import AutoModelForCausalLM, AutoTokenizer +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +import edge_tts +import asyncio +from time import sleep +import langid +from langdetect import detect + +# --- 配置huggingFace国内镜像 --- +import os +os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' + +# 参数设置 +AUDIO_RATE = 16000 # 音频采样率 +AUDIO_CHANNELS = 1 # 单声道 +CHUNK = 1024 # 音频块大小 +VAD_MODE = 3 # VAD 模式 (0-3, 数字越大越敏感) +OUTPUT_DIR = "./output" # 输出目录 +NO_SPEECH_THRESHOLD = 1 # 无效语音阈值，单位：秒 +folder_path = "./Test_QWen2_VL/" +audio_file_count = 0 + +# 确保输出目录存在 +os.makedirs(OUTPUT_DIR, exist_ok=True) +os.makedirs(folder_path, exist_ok=True) + +# 队列用于音频和视频同步缓存 +audio_queue = Queue() +video_queue = Queue() + +# 全局变量 +last_active_time = time.time() +recording_active = True +segments_to_save = [] +saved_intervals = [] +last_vad_end_time = 0 # 上次保存的 VAD 有效段结束时间 + +# 初始化 WebRTC VAD +vad = webrtcvad.Vad() +vad.set_mode(VAD_MODE) + +# 音频录制线程 +def audio_recorder(): + global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time + + p = pyaudio.PyAudio() + stream = p.open(format=pyaudio.paInt16, + channels=AUDIO_CHANNELS, + rate=AUDIO_RATE, + input=True, + frames_per_buffer=CHUNK) + + audio_buffer = [] + print("音频录制已开始") + + while recording_active: + data = stream.read(CHUNK) + audio_buffer.append(data) + + # 每 0.5 秒检测一次 VAD + if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5: + # 拼接音频数据并检测 VAD + raw_audio = b''.join(audio_buffer) + vad_result = check_vad_activity(raw_audio) + + if vad_result: + print("检测到语音活动") + last_active_time = time.time() + segments_to_save.append((raw_audio, time.time())) + else: + print("静音中...") + + audio_buffer = [] # 清空缓冲区 + + # 检查无效语音时间 + if time.time() - last_active_time > NO_SPEECH_THRESHOLD: + # 检查是否需要保存 + if segments_to_save and segments_to_save[-1][1] > last_vad_end_time: + save_audio_video() + last_active_time = time.time() + else: + pass + # print("无新增语音段，跳过保存") + + stream.stop_stream() + stream.close() + p.terminate() + +# 视频录制线程 +def video_recorder(): + global video_queue, recording_active + + cap = cv2.VideoCapture(0) # 使用默认摄像头 + print("视频录制已开始") + + while recording_active: + ret, frame = cap.read() + if ret: + video_queue.put((frame, time.time())) + + # 实时显示摄像头画面 + cv2.imshow("Real Camera", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 键退出 + break + else: + print("无法获取摄像头画面") + + cap.release() + cv2.destroyAllWindows() + +# 检测 VAD 活动 +def check_vad_activity(audio_data): + # 将音频数据分块检测 + num, rate = 0, 0.4 + step = int(AUDIO_RATE * 0.02) # 20ms 块大小 + flag_rate = round(rate * len(audio_data) // step) + + for i in range(0, len(audio_data), step): + chunk = audio_data[i:i + step] + if len(chunk) == step: + if vad.is_speech(chunk, sample_rate=AUDIO_RATE): + num += 1 + + if num > flag_rate: + return True + return False + +# 保存音频和视频 +def save_audio_video(): + pygame.mixer.init() + + global segments_to_save, video_queue, last_vad_end_time, saved_intervals + + # 全局变量，用于保存音频文件名计数 + global audio_file_count + audio_file_count += 1 + audio_output_path = f"{OUTPUT_DIR}/audio_{audio_file_count}.wav" + # audio_output_path = f"{OUTPUT_DIR}/audio_0.wav" + + if not segments_to_save: + return + + # 停止当前播放的音频 + if pygame.mixer.music.get_busy(): + pygame.mixer.music.stop() + print("检测到新的有效音，已停止当前音频播放") + + # 获取有效段的时间范围 + start_time = segments_to_save[0][1] + end_time = segments_to_save[-1][1] + + # 检查是否与之前的片段重叠 + if saved_intervals and saved_intervals[-1][1] >= start_time: + print("当前片段与之前片段重叠，跳过保存") + segments_to_save.clear() + return + + # 保存音频 + audio_frames = [seg[0] for seg in segments_to_save] + + wf = wave.open(audio_output_path, 'wb') + wf.setnchannels(AUDIO_CHANNELS) + wf.setsampwidth(2) # 16-bit PCM + wf.setframerate(AUDIO_RATE) + wf.writeframes(b''.join(audio_frames)) + wf.close() + print(f"音频保存至 {audio_output_path}") + + # Inference() + # 使用线程执行推理 + inference_thread = threading.Thread(target=Inference, args=(audio_output_path,)) + inference_thread.start() + + # 记录保存的区间 + saved_intervals.append((start_time, end_time)) + + # 清空缓冲区 + segments_to_save.clear() + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + + +# -------- SenceVoice 语音识别 --模型加载----- +model_dir = r"D:\AI\download\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) + +# --- QWen2.5大语言模型 --- +model_name = r"D:\AI\download\Qwen2.5-1.5B-Instruct" +# model_name = r'D:\AI\download\Qwen2.5-7B-Instruct-GPTQ-Int4' +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +def Inference(TEMP_AUDIO_FILE=f"{OUTPUT_DIR}/audio_0.wav"): + + # -------- SenceVoice 推理 --------- + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + # prompt = res[0]['text'].split(">")[-1] + prompt = res[0]['text'].split(">")[-1] #识别结果 + print("ASR OUT:", prompt) + # ---------SenceVoice --end---------- + # -------- 模型推理阶段，将语音识别结果作为大模型Prompt ------ + messages = [ + {"role": "system", "content": "你叫千问，是一个18岁的女大学生，性格活泼开朗，说话俏皮"}, + {"role": "user", "content": prompt}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + + generated_ids = model.generate( + **model_inputs, + max_new_tokens=512, + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + output_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + + print("answer", output_text) + + # 输入文本 + text = output_text + # 语种识别 -- langid + language, confidence = langid.classify(text) + # 语种识别 -- langdetect + # language = detect(text).split("-")[0] + + language_speaker = { + "ja" : "ja-JP-NanamiNeural", # ok + "fr" : "fr-FR-DeniseNeural", # ok + "es" : "ca-ES-JoanaNeural", # ok + "de" : "de-DE-KatjaNeural", # ok + "zh" : "zh-CN-XiaoyiNeural", # ok + "en" : "en-US-AnaNeural", # ok + } + + if language not in language_speaker.keys(): + used_speaker = "zh-CN-XiaoyiNeural" + else: + used_speaker = language_speaker[language] + print("检测到语种：", language, "使用音色：", language_speaker[language]) + + global audio_file_count + asyncio.run(amain(text, used_speaker, os.path.join(folder_path,f"sft_{audio_file_count}.mp3"))) + play_audio(f'{folder_path}/sft_{audio_file_count}.mp3') + +# 主函数 +if __name__ == "__main__": + + try: + # 启动音视频录制线程 + audio_thread = threading.Thread(target=audio_recorder) + # video_thread = threading.Thread(target=video_recorder) + audio_thread.start() + # video_thread.start() + + print("按 Ctrl+C 停止录制") + while True: + time.sleep(1) + + except KeyboardInterrupt: + print("录制停止中...") + recording_active = False + audio_thread.join() + # video_thread.join() + print("录制已停止") diff --git a/14_SenceVoice_QWen2VL_edgeTTS_realTime.py b/14_SenceVoice_QWen2VL_edgeTTS_realTime.py new file mode 100644 index 0000000..988306b --- /dev/null +++ b/14_SenceVoice_QWen2VL_edgeTTS_realTime.py @@ -0,0 +1,389 @@ +import cv2 +import pyaudio +import wave +import threading +import numpy as np +import time +from queue import Queue +import webrtcvad +import os +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +import edge_tts +import asyncio +from time import sleep + +import langid +from langdetect import detect + +# --- 配置huggingFace国内镜像 --- +import os +os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' + +# 参数设置 +AUDIO_RATE = 16000 # 音频采样率 +AUDIO_CHANNELS = 1 # 单声道 +CHUNK = 1024 # 音频块大小 +VAD_MODE = 3 # VAD 模式 (0-3, 数字越大越敏感) +OUTPUT_DIR = "./output" # 输出目录 +NO_SPEECH_THRESHOLD = 1 # 无效语音阈值，单位：秒 +audio_file_count = 0 + +# 确保输出目录存在 +os.makedirs(OUTPUT_DIR, exist_ok=True) + +# 队列用于音频和视频同步缓存 +audio_queue = Queue() +video_queue = Queue() + +# 全局变量 +last_active_time = time.time() +recording_active = True +segments_to_save = [] +saved_intervals = [] +last_vad_end_time = 0 # 上次保存的 VAD 有效段结束时间 + +# 初始化 WebRTC VAD +vad = webrtcvad.Vad() +vad.set_mode(VAD_MODE) + +# 音频录制线程 +def audio_recorder(): + global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time + + p = pyaudio.PyAudio() + stream = p.open(format=pyaudio.paInt16, + channels=AUDIO_CHANNELS, + rate=AUDIO_RATE, + input=True, + frames_per_buffer=CHUNK) + + audio_buffer = [] + print("音频录制已开始") + + while recording_active: + data = stream.read(CHUNK) + audio_buffer.append(data) + + # 每 0.5 秒检测一次 VAD + if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5: + # 拼接音频数据并检测 VAD + raw_audio = b''.join(audio_buffer) + vad_result = check_vad_activity(raw_audio) + + if vad_result: + print("检测到语音活动") + last_active_time = time.time() + segments_to_save.append((raw_audio, time.time())) + else: + print("静音中...") + + audio_buffer = [] # 清空缓冲区 + + # 检查无效语音时间 + if time.time() - last_active_time > NO_SPEECH_THRESHOLD: + # 检查是否需要保存 + if segments_to_save and segments_to_save[-1][1] > last_vad_end_time: + save_audio_video() + last_active_time = time.time() + else: + pass + # print("无新增语音段，跳过保存") + + stream.stop_stream() + stream.close() + p.terminate() + +# 视频录制线程 +def video_recorder(): + global video_queue, recording_active + + cap = cv2.VideoCapture(0) # 使用默认摄像头 + print("视频录制已开始") + + while recording_active: + ret, frame = cap.read() + if ret: + video_queue.put((frame, time.time())) + + # 实时显示摄像头画面 + cv2.imshow("Real Camera", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 键退出 + break + else: + print("无法获取摄像头画面") + + cap.release() + cv2.destroyAllWindows() + +# 检测 VAD 活动 +def check_vad_activity(audio_data): + # 将音频数据分块检测，设置有效激活率rate=40%，低于此比例当作静音段 + num, rate = 0, 0.4 + step = int(AUDIO_RATE * 0.02) # 20ms 块大小 + flag_rate = round(rate * len(audio_data) // step) + + for i in range(0, len(audio_data), step): + chunk = audio_data[i:i + step] + if len(chunk) == step: + if vad.is_speech(chunk, sample_rate=AUDIO_RATE): + num += 1 + + if num > flag_rate: + return True + return False + +# 保存音频和视频 +def save_audio_video(): + pygame.mixer.init() + global segments_to_save, video_queue, last_vad_end_time, saved_intervals + + # 全局变量，用于保存音频文件名计数 + global audio_file_count + audio_file_count += 1 + audio_output_path = f"{OUTPUT_DIR}/audio_{audio_file_count}.wav" + video_output_path = f"{OUTPUT_DIR}/video_{audio_file_count}.avi" + + if not segments_to_save: + return + + # 用于实时打断：接收到新保存文件需求，停止当前播放的音频 + if pygame.mixer.music.get_busy(): + pygame.mixer.music.stop() + print("检测到新的有效音，已停止当前音频播放") + + # 获取有效段的时间范围 + start_time = segments_to_save[0][1] + end_time = segments_to_save[-1][1] + + # 检查是否与之前的片段重叠 + if saved_intervals and saved_intervals[-1][1] >= start_time: + print("当前片段与之前片段重叠，跳过保存") + segments_to_save.clear() + return + + # 保存音频 + audio_frames = [seg[0] for seg in segments_to_save] + wf = wave.open(audio_output_path, 'wb') + wf.setnchannels(AUDIO_CHANNELS) + wf.setsampwidth(2) # 16-bit PCM + wf.setframerate(AUDIO_RATE) + wf.writeframes(b''.join(audio_frames)) + wf.close() + print(f"音频保存至 {audio_output_path}") + + # 保存视频 + video_frames = [] + while not video_queue.empty(): + frame, timestamp = video_queue.get() + if start_time <= timestamp <= end_time: + video_frames.append(frame) + + if video_frames: + out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480)) + for frame in video_frames: + out.write(frame) + out.release() + print(f"视频保存至 {video_output_path}") + + # --- 直接推理会影响录制主线程，无法实现实时打断逻辑 --- + # Inference() + + # --- 使用线程执行推理 + inference_thread = threading.Thread(target=Inference, args=(video_output_path, audio_output_path)) + inference_thread.start() + else: + pass + # print("无可保存的视频帧") + + # 记录保存的区间 + saved_intervals.append((start_time, end_time)) + + # 清空缓冲区 + segments_to_save.clear() + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + +# -------------- Load QWen2-VL Model ------------ +# default: Load the model on the available device(s) +model = Qwen2VLForConditionalGeneration.from_pretrained( + "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" +) +# ------- 设置分辨率，降低现存占用 ------- +min_pixels = 256*28*28 +max_pixels = 512*28*28 +processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) +# -------------------------------------- + +# -------- SenceVoice 语音识别 --模型加载----- +model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) +folder_path = "./Test_QWen2_VL/" + +def Inference(TEMP_VIDEO_FILE, TEMP_AUDIO_FILE): + + cap = cv2.VideoCapture(TEMP_VIDEO_FILE) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + # --- 设定视频截取帧时间比例 + S_index = [0.2, 0.4, 0.6, 0.8] + frame_index = [int(total_frames * i) for i in S_index] + # 设置视频帧位置 + for idx in frame_index: + cap.set(cv2.CAP_PROP_POS_FRAMES, idx) + ret, frame = cap.read() + if not ret: + print(f"无法读取帧索引 {idx}") + else: + # 保存帧 + file_path = os.path.join(folder_path, f"captured_image{idx}.jpg") # 设置保存路径 + cv2.imwrite(file_path, frame) + + # -------- SenceVoice 推理 --start------- + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + # prompt = res[0]['text'].split(">")[-1] + prompt = res[0]['text'].split(">")[-1] + "，回答简短一些，保持50字以内！" + print("ASR OUT:", prompt) + # ---------SenceVoice 推理--end---------- + + MODE_FLAG = 0 + # -------- QWen2-VL 模型推理 --------- 多图模式 + # Messages containing a images list as a video and a text query + if not MODE_FLAG: + messages = [ + { + "role": "user", + "content": [ + { + "type": "video", + "video": [ + f'{os.path.join(folder_path, f"captured_image{frame_index[0]}.jpg")}', + f'{os.path.join(folder_path, f"captured_image{frame_index[1]}.jpg")}', + f'{os.path.join(folder_path, f"captured_image{frame_index[2]}.jpg")}', + f'{os.path.join(folder_path, f"captured_image{frame_index[3]}.jpg")}', + ], + "fps": 1.0, + }, + {"type": "text", "text": f"{prompt}"}, + ], + } + ] + + # -------- QWen2-VL 模型推理 --------- 视频模式 + else: + messages = [ + { + "role": "user", + "content": [ + { + "type": "video", + "video": f"{TEMP_VIDEO_FILE}", + "max_pixels": 360 * 420, + "fps": 1.0, + }, + {"type": "text", "text": f"{prompt}"}, + ], + } + ] + + # Preparation for inference + text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + image_inputs, video_inputs = process_vision_info(messages) + inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + inputs = inputs.to("cuda") + + # Inference + generated_ids = model.generate(**inputs, max_new_tokens=128) + generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + output_text = processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False + ) + print(output_text) + + # 输入文本 + text = output_text[0] + # 语种识别 -- langid + language, confidence = langid.classify(text) + # 语种识别 -- langdetect -- 没做结果对应关键词映射 + # language = detect(text) + + language_speaker = { + "ja" : "ja-JP-NanamiNeural", # ok + "fr" : "fr-FR-DeniseNeural", # ok + "es" : "ca-ES-JoanaNeural", # ok + "de" : "de-DE-KatjaNeural", # ok + "zh" : "zh-CN-XiaoyiNeural", # ok + "en" : "en-US-AnaNeural", # ok + } + + if language not in language_speaker.keys(): + used_speaker = "zh-CN-XiaoyiNeural" + else: + used_speaker = language_speaker[language] + print("检测到语种：", language, "使用音色：", language_speaker[language]) + + global audio_file_count + asyncio.run(amain(text, used_speaker, os.path.join(folder_path,f"sft_{audio_file_count}.mp3"))) + play_audio(f'{folder_path}/sft_{audio_file_count}.mp3') + + # asyncio.run(amain(text, "zh-CN-YunjianNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + + # asyncio.run(amain(text, "zh-CN-shaanxi-XiaoniNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + +# 主函数 +if __name__ == "__main__": + try: + # 启动音视频录制线程 + audio_thread = threading.Thread(target=audio_recorder) + video_thread = threading.Thread(target=video_recorder) + audio_thread.start() + video_thread.start() + + print("按 Ctrl+C 停止录制") + while True: + time.sleep(1) + + except KeyboardInterrupt: + print("录制停止中...") + recording_active = False + audio_thread.join() + video_thread.join() + print("录制已停止") diff --git a/15.0_SenceVoice_kws_CAM++.py b/15.0_SenceVoice_kws_CAM++.py new file mode 100644 index 0000000..5fb0b3d --- /dev/null +++ b/15.0_SenceVoice_kws_CAM++.py @@ -0,0 +1,474 @@ +import cv2 +import pyaudio +import wave +import threading +import numpy as np +import time +from queue import Queue +import webrtcvad +import os +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from transformers import AutoModelForCausalLM, AutoTokenizer +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +import edge_tts +import asyncio +from time import sleep +import langid +from langdetect import detect +import re +from pypinyin import pinyin, Style +from modelscope.pipelines import pipeline + +# --- 配置huggingFace国内镜像 --- +import os +os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' + +# 参数设置 +AUDIO_RATE = 16000 # 音频采样率 +AUDIO_CHANNELS = 1 # 单声道 +CHUNK = 1024 # 音频块大小 +VAD_MODE = 3 # VAD 模式 (0-3, 数字越大越敏感) +OUTPUT_DIR = "./output" # 输出目录 +NO_SPEECH_THRESHOLD = 1 # 无效语音阈值，单位：秒 +folder_path = "./Test_QWen2_VL/" +audio_file_count = 0 +audio_file_count_tmp = 0 + +# 确保输出目录存在 +os.makedirs(OUTPUT_DIR, exist_ok=True) +os.makedirs(folder_path, exist_ok=True) + +# 队列用于音频和视频同步缓存 +audio_queue = Queue() +video_queue = Queue() + +# 全局变量 +last_active_time = time.time() +recording_active = True +segments_to_save = [] +saved_intervals = [] +last_vad_end_time = 0 # 上次保存的 VAD 有效段结束时间 + + +# --- 唤醒词、声纹变量配置 --- +# set_KWS = "ni hao xiao qian" +set_KWS = "shuo hua xiao qian" +flag_KWS = 0 +flag_KWS_used = 1 + +flag_sv_used = 1 +flag_sv_enroll = 0 +thred_sv = 0.35 + +# 初始化 WebRTC VAD +vad = webrtcvad.Vad() +vad.set_mode(VAD_MODE) + + +def extract_chinese_and_convert_to_pinyin(input_string): + """ + 提取字符串中的汉字，并将其转换为拼音。 + + :param input_string: 原始字符串 + :return: 转换后的拼音字符串 + """ + # 使用正则表达式提取所有汉字 + chinese_characters = re.findall(r'[\u4e00-\u9fa5]', input_string) + # 将汉字列表合并为字符串 + chinese_text = ''.join(chinese_characters) + + # 转换为拼音 + pinyin_result = pinyin(chinese_text, style=Style.NORMAL) + # 将拼音列表拼接为字符串 + pinyin_text = ' '.join([item[0] for item in pinyin_result]) + + return pinyin_text + + +# 音频录制线程 +def audio_recorder(): + global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time + + p = pyaudio.PyAudio() + stream = p.open(format=pyaudio.paInt16, + channels=AUDIO_CHANNELS, + rate=AUDIO_RATE, + input=True, + frames_per_buffer=CHUNK) + + audio_buffer = [] + print("音频录制已开始") + + while recording_active: + data = stream.read(CHUNK) + audio_buffer.append(data) + + # 每 0.5 秒检测一次 VAD + if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5: + # 拼接音频数据并检测 VAD + raw_audio = b''.join(audio_buffer) + vad_result = check_vad_activity(raw_audio) + + if vad_result: + print("检测到语音活动") + last_active_time = time.time() + segments_to_save.append((raw_audio, time.time())) + else: + print("静音中...") + + audio_buffer = [] # 清空缓冲区 + + # 检查无效语音时间 + if time.time() - last_active_time > NO_SPEECH_THRESHOLD: + # 检查是否需要保存 + if segments_to_save and segments_to_save[-1][1] > last_vad_end_time: + save_audio_video() + last_active_time = time.time() + else: + pass + # print("无新增语音段，跳过保存") + + stream.stop_stream() + stream.close() + p.terminate() + +# 视频录制线程 +def video_recorder(): + global video_queue, recording_active + + cap = cv2.VideoCapture(0) # 使用默认摄像头 + print("视频录制已开始") + + while recording_active: + ret, frame = cap.read() + if ret: + video_queue.put((frame, time.time())) + + # 实时显示摄像头画面 + cv2.imshow("Real Camera", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 键退出 + break + else: + print("无法获取摄像头画面") + + cap.release() + cv2.destroyAllWindows() + +# 检测 VAD 活动 +def check_vad_activity(audio_data): + # 将音频数据分块检测 + num, rate = 0, 0.4 + step = int(AUDIO_RATE * 0.02) # 20ms 块大小 + flag_rate = round(rate * len(audio_data) // step) + + for i in range(0, len(audio_data), step): + chunk = audio_data[i:i + step] + if len(chunk) == step: + if vad.is_speech(chunk, sample_rate=AUDIO_RATE): + num += 1 + + if num > flag_rate: + return True + return False + +# 保存音频和视频 +def save_audio_video(): + pygame.mixer.init() + + global segments_to_save, video_queue, last_vad_end_time, saved_intervals + + # 全局变量，用于保存音频文件名计数 + global audio_file_count + global flag_sv_enroll + global set_SV_enroll + + if flag_sv_enroll: + audio_output_path = f"{set_SV_enroll}/enroll_0.wav" + else: + audio_file_count += 1 + audio_output_path = f"{OUTPUT_DIR}/audio_{audio_file_count}.wav" + # audio_output_path = f"{OUTPUT_DIR}/audio_0.wav" + + if not segments_to_save: + return + + # 停止当前播放的音频 + if pygame.mixer.music.get_busy(): + pygame.mixer.music.stop() + print("检测到新的有效音，已停止当前音频播放") + + # 获取有效段的时间范围 + start_time = segments_to_save[0][1] + end_time = segments_to_save[-1][1] + + # 检查是否与之前的片段重叠 + if saved_intervals and saved_intervals[-1][1] >= start_time: + print("当前片段与之前片段重叠，跳过保存") + segments_to_save.clear() + return + + # 保存音频 + audio_frames = [seg[0] for seg in segments_to_save] + if flag_sv_enroll: + audio_length = 0.5 * len(segments_to_save) + if audio_length < 3: + print("声纹注册语音需大于3秒，请重新注册") + return 1 + + wf = wave.open(audio_output_path, 'wb') + wf.setnchannels(AUDIO_CHANNELS) + wf.setsampwidth(2) # 16-bit PCM + wf.setframerate(AUDIO_RATE) + wf.writeframes(b''.join(audio_frames)) + wf.close() + print(f"音频保存至 {audio_output_path}") + + # Inference() + + if flag_sv_enroll: + text = "声纹注册完成！现在只有你可以命令我啦！" + print(text) + flag_sv_enroll = 0 + system_introduction(text) + else: + # 使用线程执行推理 + inference_thread = threading.Thread(target=Inference, args=(audio_output_path,)) + inference_thread.start() + + # 记录保存的区间 + saved_intervals.append((start_time, end_time)) + + # 清空缓冲区 + segments_to_save.clear() + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + +import os + +def is_folder_empty(folder_path): + """ + 检测指定文件夹内是否有文件。 + + :param folder_path: 文件夹路径 + :return: 如果文件夹为空返回 True，否则返回 False + """ + # 获取文件夹中的所有条目（文件或子文件夹） + entries = os.listdir(folder_path) + # 检查是否存在文件 + for entry in entries: + # 获取完整路径 + full_path = os.path.join(folder_path, entry) + # 如果是文件，返回 False + if os.path.isfile(full_path): + return False + # 如果没有文件，返回 True + return True + + +# -------- SenceVoice 语音识别 --模型加载----- +model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) + +set_SV_enroll = r'.\SpeakerVerification_DIR\enroll_wav\\' +# -------- CAM++声纹识别 -- 模型加载 -------- +sv_pipeline = pipeline( + task='speaker-verification', + model='damo/speech_campplus_sv_zh-cn_16k-common', + model_revision='v1.0.0' +) + +# --------- QWen2.5大语言模型 --------------- +# model_name = r"E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-0.5B-Instruct" +model_name = r"E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-1.5B-Instruct" +# model_name = r'E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-7B-Instruct-GPTQ-Int4' +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +def system_introduction(text): + global audio_file_count + global folder_path + text = text + print("LLM output:", text) + used_speaker = "zh-CN-XiaoyiNeural" + asyncio.run(amain(text, used_speaker, os.path.join(folder_path,f"sft_tmp_{audio_file_count}.mp3"))) + play_audio(f'{folder_path}/sft_tmp_{audio_file_count}.mp3') + +def Inference(TEMP_AUDIO_FILE=f"{OUTPUT_DIR}/audio_0.wav"): + ''' + 1. 使用senceVoice做asr，转换为拼音，检测唤醒词 + - 首先检测声纹注册文件夹是否有注册文件，如果无，启动声纹注册 + 2. 使用CAM++做声纹识别 + - 设置固定声纹注册语音目录，每次输入音频均进行声纹对比 + 3. 以上两者均通过，则进行大模型推理 + ''' + global audio_file_count + + global set_SV_enroll + global flag_sv_enroll + global thred_sv + global flag_sv_used + + global set_KWS + global flag_KWS + global flag_KWS_used + + + # flag_info = f'{flag_sv_used}-{flag_KWS_used}' + # dict_flag_info = { + # "1-1": "您已开启声纹识别和关键词唤醒，", + # "0-1":"您已开启关键词唤醒", + # "1-0":"您已开启声纹识别", + # "0-0":"", + # } + # if flag_sv_used or flag_KWS_used: + # text = dict_flag_info[flag_info] + # system_introduction(text) + + os.makedirs(set_SV_enroll, exist_ok=True) + # --- 如果开启声纹识别，且声纹文件夹为空，则开始声纹注册。设定注册语音有效长度需大于3秒 + if flag_sv_used and is_folder_empty(set_SV_enroll): + text = f"无声纹注册文件！请先注册声纹，需大于三秒哦~" + print(text) + system_introduction(text) + flag_sv_enroll = 1 + + else: + # -------- SenceVoice 推理 --------- + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + prompt = res[0]['text'].split(">")[-1] + prompt_pinyin = extract_chinese_and_convert_to_pinyin(prompt) + print(prompt, prompt_pinyin) + + # --- 判断是否启动KWS + if not flag_KWS_used: + flag_KWS = 1 + if not flag_KWS: + if set_KWS in prompt_pinyin: + flag_KWS = 1 + + # --- KWS成功，或不设置KWS + if flag_KWS: + sv_score = sv_pipeline([os.path.join(set_SV_enroll, "enroll_0.wav"), TEMP_AUDIO_FILE], thr=thred_sv) + print(sv_score) + sv_result = sv_score['text'] + if sv_result == "yes": + prompt = res[0]['text'].split(">")[-1] + "，回答简短一些，保持50字以内！" + print("ASR OUT:", prompt) + # ---------SenceVoice --end---------- + # -------- 模型推理阶段，将语音识别结果作为大模型Prompt ------ + messages = [ + {"role": "system", "content": "你叫小千，是一个18岁的女大学生，性格活泼开朗，说话俏皮"}, + {"role": "user", "content": prompt}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + + generated_ids = model.generate( + **model_inputs, + max_new_tokens=512, + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + output_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + + print("answer", output_text) + + # 输入文本 + text = output_text + # 语种识别 -- langid + language, confidence = langid.classify(text) + # 语种识别 -- langdetect + # language = detect(text).split("-")[0] + + language_speaker = { + "ja" : "ja-JP-NanamiNeural", # ok + "fr" : "fr-FR-DeniseNeural", # ok + "es" : "ca-ES-JoanaNeural", # ok + "de" : "de-DE-KatjaNeural", # ok + "zh" : "zh-CN-XiaoyiNeural", # ok + "en" : "en-US-AnaNeural", # ok + } + + if language not in language_speaker.keys(): + used_speaker = "zh-CN-XiaoyiNeural" + else: + used_speaker = language_speaker[language] + print("检测到语种：", language, "使用音色：", language_speaker[language]) + + asyncio.run(amain(text, used_speaker, os.path.join(folder_path,f"sft_{audio_file_count}.mp3"))) + play_audio(f'{folder_path}/sft_{audio_file_count}.mp3') + else: + text = "很抱歉，你不是我的主人哦，我无法为您服务" + system_introduction(text) + else: + text = "很抱歉，唤醒词错误，我无法为您服务。请说出正确的唤醒词哦" + system_introduction(text) + +# 主函数 +if __name__ == "__main__": + + try: + # 启动音视频录制线程 + audio_thread = threading.Thread(target=audio_recorder) + # video_thread = threading.Thread(target=video_recorder) + audio_thread.start() + # video_thread.start() + + flag_info = f'{flag_sv_used}-{flag_KWS_used}' + dict_flag_info = { + "1-1": "您已开启声纹识别和关键词唤醒，", + "0-1":"您已开启关键词唤醒", + "1-0":"您已开启声纹识别", + "0-0":"", + } + if flag_sv_used or flag_KWS_used: + text = dict_flag_info[flag_info] + system_introduction(text) + + print("按 Ctrl+C 停止录制") + while True: + time.sleep(1) + + except KeyboardInterrupt: + print("录制停止中...") + recording_active = False + audio_thread.join() + # video_thread.join() + print("录制已停止") diff --git a/15.1_SenceVoice_kws_CAM++.py b/15.1_SenceVoice_kws_CAM++.py new file mode 100644 index 0000000..13bacad --- /dev/null +++ b/15.1_SenceVoice_kws_CAM++.py @@ -0,0 +1,502 @@ +import cv2 +import pyaudio +import wave +import threading +import numpy as np +import time +from queue import Queue +import webrtcvad +import os +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from transformers import AutoModelForCausalLM, AutoTokenizer +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +import edge_tts +import asyncio +from time import sleep +import langid +from langdetect import detect +import re +from pypinyin import pinyin, Style +from modelscope.pipelines import pipeline + +# --- 配置huggingFace国内镜像 --- +import os +os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com' + +# 参数设置 +AUDIO_RATE = 16000 # 音频采样率 +AUDIO_CHANNELS = 1 # 单声道 +CHUNK = 1024 # 音频块大小 +VAD_MODE = 3 # VAD 模式 (0-3, 数字越大越敏感) +OUTPUT_DIR = "./output" # 输出目录 +NO_SPEECH_THRESHOLD = 1 # 无效语音阈值，单位：秒 +folder_path = "./Test_QWen2_VL/" +audio_file_count = 0 +audio_file_count_tmp = 0 + +# 确保输出目录存在 +os.makedirs(OUTPUT_DIR, exist_ok=True) +os.makedirs(folder_path, exist_ok=True) + +# 队列用于音频和视频同步缓存 +audio_queue = Queue() +video_queue = Queue() + +# 全局变量 +last_active_time = time.time() +recording_active = True +segments_to_save = [] +saved_intervals = [] +last_vad_end_time = 0 # 上次保存的 VAD 有效段结束时间 + + +# --- 唤醒词、声纹变量配置 --- +# set_KWS = "ni hao xiao qian" +# set_KWS = "shuo hua xiao qian" +set_KWS = "ni hao qian wen" +flag_KWS = 0 + +flag_KWS_used = 1 +flag_sv_used = 1 + +flag_sv_enroll = 0 +thred_sv = 0.35 + +# 初始化 WebRTC VAD +vad = webrtcvad.Vad() +vad.set_mode(VAD_MODE) + + +def extract_chinese_and_convert_to_pinyin(input_string): + """ + 提取字符串中的汉字，并将其转换为拼音。 + + :param input_string: 原始字符串 + :return: 转换后的拼音字符串 + """ + # 使用正则表达式提取所有汉字 + chinese_characters = re.findall(r'[\u4e00-\u9fa5]', input_string) + # 将汉字列表合并为字符串 + chinese_text = ''.join(chinese_characters) + + # 转换为拼音 + pinyin_result = pinyin(chinese_text, style=Style.NORMAL) + # 将拼音列表拼接为字符串 + pinyin_text = ' '.join([item[0] for item in pinyin_result]) + + return pinyin_text + + +# 音频录制线程 +def audio_recorder(): + global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time + + p = pyaudio.PyAudio() + stream = p.open(format=pyaudio.paInt16, + channels=AUDIO_CHANNELS, + rate=AUDIO_RATE, + input=True, + frames_per_buffer=CHUNK) + + audio_buffer = [] + print("音频录制已开始") + + while recording_active: + data = stream.read(CHUNK) + audio_buffer.append(data) + + # 每 0.5 秒检测一次 VAD + if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5: + # 拼接音频数据并检测 VAD + raw_audio = b''.join(audio_buffer) + vad_result = check_vad_activity(raw_audio) + + if vad_result: + print("检测到语音活动") + last_active_time = time.time() + segments_to_save.append((raw_audio, time.time())) + else: + print("静音中...") + + audio_buffer = [] # 清空缓冲区 + + # 检查无效语音时间 + if time.time() - last_active_time > NO_SPEECH_THRESHOLD: + # 检查是否需要保存 + if segments_to_save and segments_to_save[-1][1] > last_vad_end_time: + save_audio_video() + last_active_time = time.time() + else: + pass + # print("无新增语音段，跳过保存") + + stream.stop_stream() + stream.close() + p.terminate() + +# 视频录制线程 +def video_recorder(): + global video_queue, recording_active + + cap = cv2.VideoCapture(0) # 使用默认摄像头 + print("视频录制已开始") + + while recording_active: + ret, frame = cap.read() + if ret: + video_queue.put((frame, time.time())) + + # 实时显示摄像头画面 + cv2.imshow("Real Camera", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 键退出 + break + else: + print("无法获取摄像头画面") + + cap.release() + cv2.destroyAllWindows() + +# 检测 VAD 活动 +def check_vad_activity(audio_data): + # 将音频数据分块检测 + num, rate = 0, 0.5 + step = int(AUDIO_RATE * 0.02) # 20ms 块大小 + flag_rate = round(rate * len(audio_data) // step) + + for i in range(0, len(audio_data), step): + chunk = audio_data[i:i + step] + if len(chunk) == step: + if vad.is_speech(chunk, sample_rate=AUDIO_RATE): + num += 1 + + if num > flag_rate: + return True + return False + +# 保存音频和视频 +def save_audio_video(): + pygame.mixer.init() + + global segments_to_save, video_queue, last_vad_end_time, saved_intervals + + # 全局变量，用于保存音频文件名计数 + global audio_file_count + global flag_sv_enroll + global set_SV_enroll + + if flag_sv_enroll: + audio_output_path = f"{set_SV_enroll}/enroll_0.wav" + else: + audio_file_count += 1 + audio_output_path = f"{OUTPUT_DIR}/audio_{audio_file_count}.wav" + # audio_output_path = f"{OUTPUT_DIR}/audio_0.wav" + + if not segments_to_save: + return + + # 停止当前播放的音频 + if pygame.mixer.music.get_busy(): + pygame.mixer.music.stop() + print("检测到新的有效音，已停止当前音频播放") + + # 获取有效段的时间范围 + start_time = segments_to_save[0][1] + end_time = segments_to_save[-1][1] + + # 检查是否与之前的片段重叠 + if saved_intervals and saved_intervals[-1][1] >= start_time: + print("当前片段与之前片段重叠，跳过保存") + segments_to_save.clear() + return + + # 保存音频 + audio_frames = [seg[0] for seg in segments_to_save] + if flag_sv_enroll: + audio_length = 0.5 * len(segments_to_save) + if audio_length < 3: + print("声纹注册语音需大于3秒，请重新注册") + return 1 + + wf = wave.open(audio_output_path, 'wb') + wf.setnchannels(AUDIO_CHANNELS) + wf.setsampwidth(2) # 16-bit PCM + wf.setframerate(AUDIO_RATE) + wf.writeframes(b''.join(audio_frames)) + wf.close() + print(f"音频保存至 {audio_output_path}") + + # Inference() + + if flag_sv_enroll: + text = "声纹注册完成！现在只有你可以命令我啦！" + print(text) + flag_sv_enroll = 0 + system_introduction(text) + else: + # 使用线程执行推理 + inference_thread = threading.Thread(target=Inference, args=(audio_output_path,)) + inference_thread.start() + + # 记录保存的区间 + saved_intervals.append((start_time, end_time)) + + # 清空缓冲区 + segments_to_save.clear() + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + +import os + +def is_folder_empty(folder_path): + """ + 检测指定文件夹内是否有文件。 + + :param folder_path: 文件夹路径 + :return: 如果文件夹为空返回 True，否则返回 False + """ + # 获取文件夹中的所有条目（文件或子文件夹） + entries = os.listdir(folder_path) + # 检查是否存在文件 + for entry in entries: + # 获取完整路径 + full_path = os.path.join(folder_path, entry) + # 如果是文件，返回 False + if os.path.isfile(full_path): + return False + # 如果没有文件，返回 True + return True + + +# -------- SenceVoice 语音识别 --模型加载----- +model_dir = r"D:\AI\download\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) + +# -------- CAM++声纹识别 -- 模型加载 -------- +set_SV_enroll = r'.\SpeakerVerification_DIR\enroll_wav\\' +sv_pipeline = pipeline( + task='speaker-verification', + model='damo/speech_campplus_sv_zh-cn_16k-common', + model_revision='v1.0.0' +) + +# --------- QWen2.5大语言模型 --------------- +# model_name = r"E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-0.5B-Instruct" +model_name = r"D:\AI\download\Qwen2.5-0.5B-Instruct" +# model_name = r'E:\2_PYTHON\Project\GPT\QWen\Qwen2.5-7B-Instruct-GPTQ-Int4' +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) +# ---------- 模型加载结束 ----------------------- + +class ChatMemory: + def __init__(self, max_length=2048): + self.history = [] + self.max_length = max_length # 最大输入长度 + + def add_to_history(self, user_input, model_response): + """ + 添加用户输入和模型响应到历史记录。 + """ + self.history.append(f"User: {user_input}") + self.history.append(f"system: {model_response}") + + def get_context(self): + """ + 获取拼接后的对话上下文。 + """ + context = "\n".join(self.history) + # 截断上下文，使其不超过 max_length + if len(context) > self.max_length: + context = context[-self.max_length :] + return context + +# -------- memory 初始化 -------- +memory = ChatMemory(max_length=512) + +def system_introduction(text): + global audio_file_count + global folder_path + text = text + print("LLM output:", text) + used_speaker = "zh-CN-XiaoyiNeural" + asyncio.run(amain(text, used_speaker, os.path.join(folder_path,f"sft_tmp_{audio_file_count}.mp3"))) + play_audio(f'{folder_path}/sft_tmp_{audio_file_count}.mp3') + +def Inference(TEMP_AUDIO_FILE=f"{OUTPUT_DIR}/audio_0.wav"): + ''' + 1. 使用senceVoice做asr，转换为拼音，检测唤醒词 + - 首先检测声纹注册文件夹是否有注册文件，如果无，启动声纹注册 + 2. 使用CAM++做声纹识别 + - 设置固定声纹注册语音目录，每次输入音频均进行声纹对比 + 3. 以上两者均通过，则进行大模型推理 + ''' + global audio_file_count + + global set_SV_enroll + global flag_sv_enroll + global thred_sv + global flag_sv_used + + global set_KWS + global flag_KWS + global flag_KWS_used + + os.makedirs(set_SV_enroll, exist_ok=True) + # --- 如果开启声纹识别，且声纹文件夹为空，则开始声纹注册。设定注册语音有效长度需大于3秒 + if flag_sv_used and is_folder_empty(set_SV_enroll): + text = f"无声纹注册文件！请先注册声纹，需大于三秒哦~" + print(text) + system_introduction(text) + flag_sv_enroll = 1 + + else: + # -------- SenceVoice 推理 --------- + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + prompt = res[0]['text'].split(">")[-1] + prompt_pinyin = extract_chinese_and_convert_to_pinyin(prompt) + print(prompt, prompt_pinyin) + + # --- 判断是否启动KWS + if not flag_KWS_used: + flag_KWS = 1 + if not flag_KWS: + if set_KWS in prompt_pinyin: + flag_KWS = 1 + + # --- KWS成功，或不设置KWS + if flag_KWS: + sv_score = sv_pipeline([os.path.join(set_SV_enroll, "enroll_0.wav"), TEMP_AUDIO_FILE], thr=thred_sv) + print(sv_score) + sv_result = sv_score['text'] + if sv_result == "yes": + + # --- 读取历史对话 --- + context = memory.get_context() + + # prompt_tmp = res[0]['text'].split(">")[-1] + "，回答简短一些，保持50字以内！" + prompt_tmp = res[0]['text'].split(">")[-1] + prompt = f"{context}\nUser:{prompt_tmp}\n" + + print("History:", context) + print("ASR OUT:", prompt) + # ---------SenceVoice --end---------- + # -------- 模型推理阶段，将语音识别结果作为大模型Prompt ------ + messages = [ + {"role": "system", "content": "你叫小千，是一个18岁的女大学生，性格活泼开朗，说话俏皮简洁。"}, + {"role": "user", "content": prompt}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + + generated_ids = model.generate( + **model_inputs, + max_new_tokens=512, + ) + generated_ids = [ + output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) + ] + + output_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + + print("answer", output_text) + + # -------- 更新记忆库 ----- + memory.add_to_history(prompt_tmp, output_text) + + # 输入文本 + text = output_text + # 语种识别 -- langid + language, confidence = langid.classify(text) + # 语种识别 -- langdetect + # language = detect(text).split("-")[0] + + language_speaker = { + "ja" : "ja-JP-NanamiNeural", # ok + "fr" : "fr-FR-DeniseNeural", # ok + "es" : "ca-ES-JoanaNeural", # ok + "de" : "de-DE-KatjaNeural", # ok + "zh" : "zh-CN-XiaoyiNeural", # ok + "en" : "en-US-AnaNeural", # ok + } + + if language not in language_speaker.keys(): + used_speaker = "zh-CN-XiaoyiNeural" + else: + used_speaker = language_speaker[language] + print("检测到语种：", language, "使用音色：", language_speaker[language]) + + asyncio.run(amain(text, used_speaker, os.path.join(folder_path,f"sft_{audio_file_count}.mp3"))) + play_audio(f'{folder_path}/sft_{audio_file_count}.mp3') + else: + text = "很抱歉，声纹验证失败，我无法为您服务" + print(text) + # system_introduction(text) + else: + text = "很抱歉，唤醒词错误，请说出正确的唤醒词哦" + system_introduction(text) + +# 主函数 +if __name__ == "__main__": + + try: + # 启动音视频录制线程 + audio_thread = threading.Thread(target=audio_recorder) + # video_thread = threading.Thread(target=video_recorder) + audio_thread.start() + # video_thread.start() + + flag_info = f'{flag_sv_used}-{flag_KWS_used}' + dict_flag_info = { + "1-1": "您已开启声纹识别和关键词唤醒，", + "0-1":"您已开启关键词唤醒", + "1-0":"您已开启声纹识别", + "0-0":"", + } + if flag_sv_used or flag_KWS_used: + text = dict_flag_info[flag_info] + system_introduction(text) + + print("按 Ctrl+C 停止录制") + while True: + time.sleep(1) + + except KeyboardInterrupt: + print("录制停止中...") + recording_active = False + audio_thread.join() + # video_thread.join() + print("录制已停止") diff --git a/1_Inference_CosyVoice.py b/1_Inference_CosyVoice.py new file mode 100644 index 0000000..b1c5ac3 --- /dev/null +++ b/1_Inference_CosyVoice.py @@ -0,0 +1,35 @@ +from cosyvoice.cli.cosyvoice import CosyVoice +from cosyvoice.utils.file_utils import load_wav +import torchaudio +cosyvoice = CosyVoice(r'D:\AI\download\CosyVoice-300M', load_jit=True, load_onnx=False, fp16=True) +print(cosyvoice.list_avaliable_spks()) +prompt_speech_16k = load_wav('vocal_3.mp3_10.wav_0006151680_0006360320.wav', 16000) +for i, j in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜', '可以动动你的小手点个关注，感谢各位好哥哥，如果之后有新消息，我还会在更新呢。', prompt_speech_16k, stream=False)): + torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], 22050) + +# change stream=True for chunk stream inference +# out = cosyvoice.inference_sft('你好，我是通义生成式语音大模型，请问有什么可以帮您的吗？', '中文女', stream=False) +# torchaudio.save('sft_0.wav', out['tts_speech'], 22050) + +# for i, j in enumerate(cosyvoice.inference_sft('你好，我是通义生成式语音大模型，请问有什么可以帮您的吗？', '中文女', stream=False)): +# torchaudio.save('sft_{}.wav'.format(i), j['tts_speech'], 22050) + +# cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-25Hz') # or change to pretrained_models/CosyVoice-300M for 50Hz inference +# # zero_shot usage, <|zh|><|en|><|jp|><|yue|><|ko|> for Chinese/English/Japanese/Cantonese/Korean +# prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000) +# for i, j in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=False)): +# torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], 22050) +# # cross_lingual usage +# prompt_speech_16k = load_wav('cross_lingual_prompt.wav', 16000) +# for i, j in enumerate(cosyvoice.inference_cross_lingual('<|en|>And then later on, fully acquiring that company. So keeping management in line, interest in line with the asset that\'s coming into the family is a reason why sometimes we don\'t buy the whole thing.', prompt_speech_16k, stream=False)): +# torchaudio.save('cross_lingual_{}.wav'.format(i), j['tts_speech'], 22050) +# # vc usage +# prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000) +# source_speech_16k = load_wav('cross_lingual_prompt.wav', 16000) +# for i, j in enumerate(cosyvoice.inference_vc(source_speech_16k, prompt_speech_16k, stream=False)): +# torchaudio.save('vc_{}.wav'.format(i), j['tts_speech'], 22050) + +# cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-Instruct') +# # instruct usage, support [laughter][breath] +# for i, j in enumerate(cosyvoice.inference_instruct('在面对挑战时，他展现了非凡的勇气与智慧。', '中文男', 'Theo \'Crimson\', is a fiery, passionate rebel leader. Fights with fervor for justice, but struggles with impulsiveness.', stream=False)): +# torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], 22050) \ No newline at end of file diff --git a/2_record_test.py b/2_record_test.py new file mode 100644 index 0000000..feb3aa8 --- /dev/null +++ b/2_record_test.py @@ -0,0 +1,46 @@ +import pygame +import time +import sys +import sounddevice as sd +from scipy.io.wavfile import write +import numpy as np + +def record_audio(filename="output.wav", sample_rate=44100): + print("按下 Enter 开始录音...") + input() # 等待用户按下 Enter 键开始录音 + print("录音中... 按下 Enter 键结束录音") + + # 开始录音 + recording = [] + try: + def callback(indata, frames, time, status): + recording.append(indata.copy()) + with sd.InputStream(samplerate=sample_rate, channels=1, callback=callback): + input() # 等待用户再次按下 Enter 键结束录音 + except Exception as e: + print(f"录音出现错误: {e}") + return + + # 将录音数据合并并保存为 WAV 文件 + audio_data = np.concatenate(recording, axis=0) + write(filename, sample_rate, (audio_data * 32767).astype(np.int16)) + print(f"录音已保存为 {filename}") + + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +# 使用函数录音，作为输入 +record_audio("my_recording.wav") +play_audio('my_recording.wav') \ No newline at end of file diff --git a/3.04backend_service.py b/3.04backend_service.py new file mode 100644 index 0000000..863017a --- /dev/null +++ b/3.04backend_service.py @@ -0,0 +1,155 @@ +from flask import Flask, request, jsonify, send_from_directory +from flask_cors import CORS +import time +import os +from transformers import AutoTokenizer, AutoModelForCausalLM +from funasr import AutoModel +import edge_tts +import asyncio +import langid +import tempfile + +app = Flask(__name__) +CORS(app) + +# 配置参数 +AUDIO_RATE = 16000 +OUTPUT_DIR = "./output" +os.makedirs(OUTPUT_DIR, exist_ok=True) + +# 初始化模型 +print("Loading models...") +model_dir = "D:/AI/download/SenseVoiceSmall" +model_senceVoice = AutoModel(model=model_dir, trust_remote_code=True) + +# 加载Qwen2.5大语言模型 +model_name = "D:/AI/download/Qwen2.5-1.5B-Instruct" +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) +print("All models loaded!") + +# 语言映射 +language_speaker = { + "ja": "ja-JP-NanamiNeural", + "fr": "fr-FR-DeniseNeural", + "es": "ca-ES-JoanaNeural", + "de": "de-DE-KatjaNeural", + "zh": "zh-CN-XiaoyiNeural", + "en": "en-US-AnaNeural", +} + +# 全局变量 +def process_audio(input_path): + # 语音识别 + res = model_senceVoice.generate( + input=input_path, + cache={}, + language="auto", + use_itn=False, + ) + prompt = res[0]['text'].split(">")[-1] + print("ASR OUT:", prompt) + + # 大模型处理 + messages = [ + {"role": "system", "content": "你叫千问，是一个18岁的女大学生，性格活泼开朗，说话俏皮"}, + {"role": "user", "content": prompt}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + generated_ids = model.generate(**model_inputs, max_new_tokens=512) + generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids + in zip(model_inputs.input_ids, generated_ids)] + output_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + print("Answer:", output_text) + + # 语种识别 + lang, _ = langid.classify(output_text) + speaker = language_speaker.get(lang, "zh-CN-XiaoyiNeural") + + # 语音合成 + output_file = os.path.join(OUTPUT_DIR, f"response_{int(time.time())}.mp3") + asyncio.run(edge_tts.Communicate(output_text, speaker).save(output_file)) + + return output_file + +# 新增ASR专用接口 +@app.route('/asr', methods=['POST']) +def handle_asr(): + if 'audio' not in request.files: + return jsonify({"error": "No audio file provided"}), 400 + + try: + audio_file = request.files['audio'] + with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: + audio_file.save(tmp.name) + + # 语音识别 + res = model_senceVoice.generate( + input=tmp.name, + cache={}, + language="auto", + use_itn=False, + ) + asr_text = res[0]['text'].split(">")[-1] + + return jsonify({ + "asr_text": asr_text + }) + + except Exception as e: + return jsonify({"error": str(e)}), 500 + +# 大模型处理接口 +@app.route('/generate', methods=['POST']) +def handle_generate(): + try: + data = request.get_json() + asr_text = data.get('asr_text', '') + + if not asr_text: + return jsonify({"error": "No ASR text provided"}), 400 + + # 大模型处理 + messages = [ + {"role": "system", "content": "你叫千问，是一个18岁的女大学生，性格活泼开朗，说话俏皮"}, + {"role": "user", "content": asr_text}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + generated_ids = model.generate(**model_inputs, max_new_tokens=512) + generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids + in zip(model_inputs.input_ids, generated_ids)] + answer_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + + lang, _ = langid.classify(answer_text) + speaker = language_speaker.get(lang, "zh-CN-XiaoyiNeural") + output_file = os.path.join(OUTPUT_DIR, f"response_{int(time.time())}.mp3") + asyncio.run(edge_tts.Communicate(answer_text, speaker).save(output_file)) + + return jsonify({ + "answer_text": answer_text, + "audio_url": f"/audio/{os.path.basename(output_file)}" + }) + + except Exception as e: + return jsonify({"error": str(e)}), 500 + +@app.route('/audio/') +def get_audio(filename): + return send_from_directory(OUTPUT_DIR, filename) + +if __name__ == '__main__': + app.run(port=5000, threaded=True) \ No newline at end of file diff --git a/3.07backend_service.py b/3.07backend_service.py new file mode 100644 index 0000000..90f9f38 --- /dev/null +++ b/3.07backend_service.py @@ -0,0 +1,158 @@ +from flask import Flask, request, jsonify, send_from_directory +from flask_cors import CORS +import time +import os +import logging +from transformers import AutoTokenizer, AutoModelForCausalLM +from funasr import AutoModel +import edge_tts +import asyncio +import langid +import tempfile + +app = Flask(__name__) +CORS(app) + +# 配置日志 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', + handlers=[ + logging.FileHandler('app.log'), # 文件日志 + logging.StreamHandler() # 控制台日志 + ] +) + +# 配置参数 +AUDIO_RATE = 16000 +OUTPUT_DIR = "./output" +os.makedirs(OUTPUT_DIR, exist_ok=True) + +# 初始化模型 +app.logger.info("Loading models...") +model_dir = "D:/AI/download/SenseVoiceSmall" +model_senceVoice = AutoModel(model=model_dir, trust_remote_code=True) + +# 加载Qwen2.5大语言模型 +model_name = "D:/AI/download/Qwen2.5-1.5B-Instruct" +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) +app.logger.info("All models loaded!") + +# 语言映射 +language_speaker = { + "ja": "ja-JP-NanamiNeural", + "fr": "fr-FR-DeniseNeural", + "es": "ca-ES-JoanaNeural", + "de": "de-DE-KatjaNeural", + "zh": "zh-CN-XiaoyiNeural", + "en": "en-US-AnaNeural", +} + +# ---------------------- 接口路由 ---------------------- +@app.route('/asr', methods=['POST']) +def handle_asr(): + """处理语音识别请求""" + if 'audio' not in request.files: + return jsonify({"error": "No audio file provided"}), 400 + + try: + audio_file = request.files['audio'] + app.logger.info(f"Received audio file: {audio_file.filename}") + + with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp: + audio_file.save(tmp.name) + + # 语音识别 + res = model_senceVoice.generate( + input=tmp.name, + cache={}, + language="auto", + use_itn=False, + ) + asr_text = res[0]['text'].split(">")[-1] + app.logger.info(f"ASR识别结果: {asr_text}") + + return jsonify({"asr_text": asr_text}) + + except Exception as e: + app.logger.error(f"ASR处理异常: {str(e)}") + return jsonify({"error": str(e)}), 500 + +@app.route('/generate_text', methods=['POST']) +def handle_generate_text(): + """处理大模型文本生成请求""" + try: + data = request.get_json() + asr_text = data.get('asr_text', '') + app.logger.info(f"收到ASR文本: {asr_text}") + + if not asr_text: + return jsonify({"error": "No ASR text provided"}), 400 + + # 构建对话模板 + messages = [ + {"role": "system", "content": "你叫千问，是一个18岁的女大学生，性格活泼开朗，说话俏皮"}, + {"role": "user", "content": asr_text}, + ] + text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + ) + + # 生成回复 + model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + generated_ids = model.generate(**model_inputs, max_new_tokens=512) + generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids + in zip(model_inputs.input_ids, generated_ids)] + answer_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] + app.logger.info(f"大模型回复: {answer_text}") + + return jsonify({"answer_text": answer_text}) + + except Exception as e: + app.logger.error(f"文本生成异常: {str(e)}") + return jsonify({"error": str(e)}), 500 + +@app.route('/generate_audio', methods=['POST']) +def handle_generate_audio(): + """处理语音合成请求""" + try: + data = request.get_json() + answer_text = data.get('answer_text', '') + app.logger.info(f"收到待合成文本: {answer_text}") + + if not answer_text: + return jsonify({"error": "No answer text provided"}), 400 + + # 语种识别 + lang, _ = langid.classify(answer_text) + speaker = language_speaker.get(lang, "zh-CN-XiaoyiNeural") + app.logger.info(f"识别到语言: {lang}, 使用发音人: {speaker}") + + # 语音合成 + output_file = os.path.join(OUTPUT_DIR, f"response_{int(time.time())}.mp3") + asyncio.run(edge_tts.Communicate(answer_text, speaker).save(output_file)) + app.logger.info(f"语音合成完成，保存路径: {output_file}") + + return jsonify({ + "audio_url": f"/audio/{os.path.basename(output_file)}" + }) + + except Exception as e: + app.logger.error(f"语音合成异常: {str(e)}") + return jsonify({"error": str(e)}), 500 + +@app.route('/audio/') +def get_audio(filename): + """音频文件下载接口""" + return send_from_directory(OUTPUT_DIR, filename) + +if __name__ == '__main__': + app.logger.info("服务启动，端口：5000") + app.run(port=5000, threaded=True) \ No newline at end of file diff --git a/3_Inference_edgeTTS.py b/3_Inference_edgeTTS.py new file mode 100644 index 0000000..ebe5ec2 --- /dev/null +++ b/3_Inference_edgeTTS.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python3 + +""" +Basic example of edge_tts usage. +""" + +import asyncio + +import edge_tts + +# TEXT = "今日特别报道，伊朗、舍沙特阿拉伯在北京中方见证下，决定恢复外交关系，重启大使馆并互相派驻外交大使" +# VOICE = "zh-CN-YunxiNeural" +# OUTPUT_FILE = "test_male.mp3" + +# TEXT = "はそれぞれ" +# VOICE = "ja-JP-NanamiNeural" +# OUTPUT_FILE = "test_male.mp3" + +# TEXT = "Estoy bien, gracias. ¿Y tú?" +# VOICE = "ca-ES-JoanaNeural" +# OUTPUT_FILE = "test_male.mp3" + +# TEXT = "Ça va bien, merci. Et toi ?" +# VOICE = "fr-FR-DeniseNeural" +# OUTPUT_FILE = "test_male.mp3" + +TEXT = "hello, whats your name" +VOICE = "en-US-AnaNeural" +OUTPUT_FILE = "test_male.mp3" + +async def amain() -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + + +if __name__ == "__main__": + asyncio.run(amain()) + +''' +Name: zh-CN-XiaoyiNeural +Gender: Female + +Name: zh-CN-YunjianNeural +Gender: Male + +Name: zh-CN-YunxiNeural +Gender: Male + +Name: zh-CN-YunxiaNeural +Gender: Male + +Name: zh-CN-YunyangNeural +Gender: Male + +Name: zh-CN-liaoning-XiaobeiNeural +Gender: Female + +Name: zh-CN-shaanxi-XiaoniNeural +Gender: Female + +Name: zh-HK-HiuGaaiNeural +Gender: Female + +Name: zh-HK-HiuMaanNeural +Gender: Female + +Name: zh-HK-WanLungNeural +Gender: Male + +Name: zh-TW-HsiaoChenNeural +Gender: Female +''' \ No newline at end of file diff --git a/4_Inference_QWen2Audio.py b/4_Inference_QWen2Audio.py new file mode 100644 index 0000000..8240100 --- /dev/null +++ b/4_Inference_QWen2Audio.py @@ -0,0 +1,49 @@ +from io import BytesIO +from urllib.request import urlopen +import librosa +from transformers import Qwen2AudioForConditionalGeneration, AutoProcessor + +model_name = r".\QWen\Qwen2-Audio-7B-Instruct" +processor = AutoProcessor.from_pretrained(model_name) +model = Qwen2AudioForConditionalGeneration.from_pretrained(model_name, device_map="cuda") + +# conversation = [ +# {"role": "user", "content": [ +# {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/guess_age_gender.wav"}, +# ]}, +# {"role": "assistant", "content": "Yes, the speaker is female and in her twenties."}, +# {"role": "user", "content": [ +# {"type": "audio", "audio_url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-Audio/audio/translate_to_chinese.wav"}, +# ]}, +# ] + +# 定义对话 +conversation = [ + {"role": "user", "content": [ + {"type": "audio", "audio_path": r".\QWen\Qwen2-Audio-7B-Instruct\guess_age_gender.wav"}, + ]}, + {"role": "assistant", "content": "Yes, the speaker is female and in her twenties."}, + {"role": "user", "content": [ + {"type": "audio", "audio_path": r".\QWen\Qwen2-Audio-7B-Instruct\translate_to_chinese.wav"}, + ]}, +] + +text = processor.apply_chat_template(conversation, add_generation_prompt=True, tokenize=False) +audios = [] +for message in conversation: + if isinstance(message["content"], list): + for ele in message["content"]: + if ele["type"] == "audio": + audios.append(librosa.load( + BytesIO(urlopen(ele['audio_url']).read()), + sr=processor.feature_extractor.sampling_rate)[0] + ) + +inputs = processor(text=text, audios=audios, return_tensors="pt", padding=True) +inputs.input_ids = inputs.input_ids.to("cuda") + +generate_ids = model.generate(**inputs, max_length=256) +generate_ids = generate_ids[:, inputs.input_ids.size(1):] + +response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] +print("Answer:", response) \ No newline at end of file diff --git a/5_pyttsx3_test.py b/5_pyttsx3_test.py new file mode 100644 index 0000000..1cacc2a --- /dev/null +++ b/5_pyttsx3_test.py @@ -0,0 +1,21 @@ +import pyttsx3 +# 初始化 TTS 引擎 +engine = pyttsx3.init() + +voices = engine.getProperty('voices') +# 打印出所有的音色信息 +for voice in voices: + print(f'id = {voice.id}----name = {voice.name}') + +# 设置语音属性 +engine.setProperty('rate', 150) # 语速engine.setProperty('volume', 0.9) # 音量（0.0 到 1.0） +# 选择语音 +voices = engine.getProperty('voices') +print(voices) +engine.setProperty('voice', voices[0].id) # 使用第一个语音 +# 输入文本 +text = "你好，今天天气很好，适合爬山" +# 朗读文本 +engine.say(text) +# 等待朗读完成 +engine.runAndWait() diff --git a/6_Inference_funasr.py b/6_Inference_funasr.py new file mode 100644 index 0000000..83a6c46 --- /dev/null +++ b/6_Inference_funasr.py @@ -0,0 +1,28 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. +# MIT License (https://opensource.org/licenses/MIT) + +import sys +from funasr import AutoModel + +model_dir = r".\QWen\pretrained_models\SenseVoiceSmall" +input_file = ( + "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" +) + +model = AutoModel( + model=model_dir, + trust_remote_code=True, +) + +res = model.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, +) + +print(res) +# import pdb; pdb.set_trace() +print(res[0]['text'].split(">")[-1]) diff --git a/7.1_test_record_AV.py b/7.1_test_record_AV.py new file mode 100644 index 0000000..5dfaaa4 --- /dev/null +++ b/7.1_test_record_AV.py @@ -0,0 +1,110 @@ +import cv2 +import pyaudio +import wave +import threading +import ffmpeg +import os + +# 配置音频参数 +AUDIO_FORMAT = pyaudio.paInt16 +CHANNELS = 1 +RATE = 44100 +CHUNK = 1024 + +# 配置视频参数 +FRAME_WIDTH = 640 +FRAME_HEIGHT = 480 +FRAME_RATE = 20.0 + +# 文件保存路径 +TEMP_AUDIO_FILE = "temp_audio.wav" +TEMP_VIDEO_FILE = "temp_video.avi" +OUTPUT_FILE = "output.mp4" + +# 音频录制线程 +def record_audio(stop_event): + audio = pyaudio.PyAudio() + stream = audio.open(format=AUDIO_FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK) + frames = [] + print("开始录音...") + + while not stop_event.is_set(): + data = stream.read(CHUNK) + frames.append(data) + + print("录音结束。") + stream.stop_stream() + stream.close() + audio.terminate() + + # 保存音频 + with wave.open(TEMP_AUDIO_FILE, 'wb') as wf: + wf.setnchannels(CHANNELS) + wf.setsampwidth(audio.get_sample_size(AUDIO_FORMAT)) + wf.setframerate(RATE) + wf.writeframes(b''.join(frames)) + +# 视频录制线程 +def record_video(stop_event): + cap = cv2.VideoCapture(0) + cap.set(cv2.CAP_PROP_FRAME_WIDTH, FRAME_WIDTH) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, FRAME_HEIGHT) + cap.set(cv2.CAP_PROP_FPS, FRAME_RATE) + + fourcc = cv2.VideoWriter_fourcc(*'XVID') + out = cv2.VideoWriter(TEMP_VIDEO_FILE, fourcc, FRAME_RATE, (FRAME_WIDTH, FRAME_HEIGHT)) + print("开始录像...") + + while not stop_event.is_set(): + ret, frame = cap.read() + if ret: + out.write(frame) + cv2.imshow('Recording Video', frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 退出摄像头窗口 + stop_event.set() + else: + break + + print("录像结束。") + cap.release() + out.release() + cv2.destroyAllWindows() + +# 合并音视频 +def merge_audio_video(audio_file, video_file, output_file): + print("正在合并音频和视频...") + ffmpeg.input(video_file).output(audio_file, output_file, vcodec='copy', acodec='aac', strict='experimental').run(overwrite_output=True) + print(f"合并完成，文件保存为: {output_file}") + +# 主函数 +def main(): + stop_event = threading.Event() + + # 启动音频和视频录制线程 + audio_thread = threading.Thread(target=record_audio, args=(stop_event,)) + video_thread = threading.Thread(target=record_video, args=(stop_event,)) + + print("按 Enter 键开始录制...") + input() # 等待用户按下 Enter 键 + print("录制中... 再次按 Enter 键停止录制。") + + audio_thread.start() + video_thread.start() + + input() # 等待用户再次按下 Enter 键 + stop_event.set() + + audio_thread.join() + video_thread.join() + + # # 合并音频和视频 + # merge_audio_video(TEMP_AUDIO_FILE, TEMP_VIDEO_FILE, OUTPUT_FILE) + + # # 清理临时文件 + # os.remove(TEMP_AUDIO_FILE) + # os.remove(TEMP_VIDEO_FILE) + + print("录制完成！") + +if __name__ == "__main__": + main() diff --git a/7.2_test_record_QWen2_VL_AV.py b/7.2_test_record_QWen2_VL_AV.py new file mode 100644 index 0000000..55a8dc1 --- /dev/null +++ b/7.2_test_record_QWen2_VL_AV.py @@ -0,0 +1,189 @@ +import cv2 +import pyaudio +import wave +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel + +# 配置音频参数 +AUDIO_FORMAT = pyaudio.paInt16 +CHANNELS = 1 +RATE = 44100 +CHUNK = 1024 + +# 配置视频参数 +FRAME_WIDTH = 640 +FRAME_HEIGHT = 480 +FRAME_RATE = 20.0 + +# 文件保存路径 +TEMP_AUDIO_FILE = "temp_audio.wav" +TEMP_VIDEO_FILE = "temp_video.avi" +# OUTPUT_FILE = "output.mp4" + +# 音频录制线程 +def record_audio(stop_event): + audio = pyaudio.PyAudio() + stream = audio.open(format=AUDIO_FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK) + frames = [] + print("开始录音...") + + while not stop_event.is_set(): + data = stream.read(CHUNK) + frames.append(data) + + print("录音结束。") + stream.stop_stream() + stream.close() + audio.terminate() + + # 保存音频 + with wave.open(TEMP_AUDIO_FILE, 'wb') as wf: + wf.setnchannels(CHANNELS) + wf.setsampwidth(audio.get_sample_size(AUDIO_FORMAT)) + wf.setframerate(RATE) + wf.writeframes(b''.join(frames)) + +# 视频录制线程 +def record_video(stop_event): + cap = cv2.VideoCapture(0) + cap.set(cv2.CAP_PROP_FRAME_WIDTH, FRAME_WIDTH) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, FRAME_HEIGHT) + cap.set(cv2.CAP_PROP_FPS, FRAME_RATE) + + fourcc = cv2.VideoWriter_fourcc(*'XVID') + out = cv2.VideoWriter(TEMP_VIDEO_FILE, fourcc, FRAME_RATE, (FRAME_WIDTH, FRAME_HEIGHT)) + print("开始录像...") + + while not stop_event.is_set(): + ret, frame = cap.read() + if ret: + out.write(frame) + cv2.imshow('Recording Video', frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 退出摄像头窗口 + stop_event.set() + else: + break + + print("录像结束。") + cap.release() + out.release() + cv2.destroyAllWindows() + +# 合并音视频 +def merge_audio_video(audio_file, video_file, output_file): + print("正在合并音频和视频...") + ffmpeg.input(video_file).output(audio_file, output_file, vcodec='copy', acodec='aac', strict='experimental').run(overwrite_output=True) + print(f"合并完成，文件保存为: {output_file}") + +# 主函数 +def main(): + stop_event = threading.Event() + + # 启动音频和视频录制线程 + audio_thread = threading.Thread(target=record_audio, args=(stop_event,)) + video_thread = threading.Thread(target=record_video, args=(stop_event,)) + + print("按 Enter 键开始录制...") + input() # 等待用户按下 Enter 键 + print("录制中... 再次按 Enter 键停止录制。") + + audio_thread.start() + video_thread.start() + + input() # 等待用户再次按下 Enter 键 + stop_event.set() + + audio_thread.join() + video_thread.join() + + # # 合并音频和视频 + # merge_audio_video(TEMP_AUDIO_FILE, TEMP_VIDEO_FILE, OUTPUT_FILE) + + # # 清理临时文件 + # os.remove(TEMP_AUDIO_FILE) + # os.remove(TEMP_VIDEO_FILE) + + print("录制完成！") + + + +if __name__ == "__main__": + main() + file_path = "captured_image.jpg" # 设置保存路径 + cap = cv2.VideoCapture(TEMP_VIDEO_FILE) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + frame_index = int(total_frames // 2) + # 设置视频帧位置 + cap.set(cv2.CAP_PROP_POS_FRAMES, frame_index) + ret, frame = cap.read() + if not ret: + print(f"无法读取帧索引 {frame_index}") + else: + # 显示帧 + cv2.imwrite(file_path, frame) + # cv2.imshow(f"Frame {frame_index}", frame) + + # -------------- Load QWen2-VL Model ------------ + # default: Load the model on the available device(s) + model = Qwen2VLForConditionalGeneration.from_pretrained( + "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" + ) + # ------- 设置分辨率，降低现存占用 ------- + min_pixels = 256*28*28 + max_pixels = 1280*28*28 + processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) + # -------------------------------------- + + # -------- SenceVoice 语音识别 ------- + model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall" + model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + prompt = res[0]['text'].split(">")[-1] + # ---------SenceVoice --end---------- + + # -------- QWen2-VL 模型推理 --------- + messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": f"{file_path}", + }, + {"type": "text", "text": f"{prompt}"}, + ], + } + ] + + # Preparation for inference + text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + image_inputs, video_inputs = process_vision_info(messages) + inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + inputs = inputs.to("cuda") + + # Inference: Generation of the output + generated_ids = model.generate(**inputs, max_new_tokens=128) + generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + output_text = processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False + ) + print(output_text) diff --git a/7.3_test_record_QWen2_VL_AV_TTS.py b/7.3_test_record_QWen2_VL_AV_TTS.py new file mode 100644 index 0000000..2a6833b --- /dev/null +++ b/7.3_test_record_QWen2_VL_AV_TTS.py @@ -0,0 +1,235 @@ +import os +import cv2 +import pyaudio +import wave +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +import time +import edge_tts +import asyncio +from time import sleep + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + +# 配置音频参数 +AUDIO_FORMAT = pyaudio.paInt16 +CHANNELS = 1 +RATE = 44100 +CHUNK = 1024 + +# 配置视频参数 +FRAME_WIDTH = 640 +FRAME_HEIGHT = 480 +FRAME_RATE = 20.0 + +# 文件保存路径 +TEMP_AUDIO_FILE = "temp_audio.wav" +TEMP_VIDEO_FILE = "temp_video.avi" +# OUTPUT_FILE = "output.mp4" + +# 音频录制线程 +def record_audio(stop_event): + # time.sleep(5) + audio = pyaudio.PyAudio() + stream = audio.open(format=AUDIO_FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK) + frames = [] + print("开始录音...") + + while not stop_event.is_set(): + data = stream.read(CHUNK) + frames.append(data) + + print("录音结束。") + stream.stop_stream() + stream.close() + audio.terminate() + + # 保存音频 + with wave.open(TEMP_AUDIO_FILE, 'wb') as wf: + wf.setnchannels(CHANNELS) + wf.setsampwidth(audio.get_sample_size(AUDIO_FORMAT)) + wf.setframerate(RATE) + wf.writeframes(b''.join(frames)) + +# 视频录制线程 +def record_video(stop_event): + # time.sleep(5) + cap = cv2.VideoCapture(0) + cap.set(cv2.CAP_PROP_FRAME_WIDTH, FRAME_WIDTH) + cap.set(cv2.CAP_PROP_FRAME_HEIGHT, FRAME_HEIGHT) + cap.set(cv2.CAP_PROP_FPS, FRAME_RATE) + + fourcc = cv2.VideoWriter_fourcc(*'XVID') + out = cv2.VideoWriter(TEMP_VIDEO_FILE, fourcc, FRAME_RATE, (FRAME_WIDTH, FRAME_HEIGHT)) + print("开始录像...") + + while not stop_event.is_set(): + ret, frame = cap.read() + if ret: + out.write(frame) + cv2.imshow('Recording Video', frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 退出摄像头窗口 + stop_event.set() + else: + break + + print("录像结束。") + cap.release() + out.release() + cv2.destroyAllWindows() + +# 合并音视频 +def merge_audio_video(audio_file, video_file, output_file): + print("正在合并音频和视频...") + ffmpeg.input(video_file).output(audio_file, output_file, vcodec='copy', acodec='aac', strict='experimental').run(overwrite_output=True) + print(f"合并完成，文件保存为: {output_file}") + +# 主函数 +def main(): + stop_event = threading.Event() + + # 启动音频和视频录制线程 + audio_thread = threading.Thread(target=record_audio, args=(stop_event,)) + video_thread = threading.Thread(target=record_video, args=(stop_event,)) + + print("按 Enter 键开始录制...") + input() # 等待用户按下 Enter 键 + print("录制中... 再次按 Enter 键停止录制。") + + audio_thread.start() + video_thread.start() + + input() # 等待用户再次按下 Enter 键 + stop_event.set() + + audio_thread.join() + video_thread.join() + + # # 合并音频和视频 + # merge_audio_video(TEMP_AUDIO_FILE, TEMP_VIDEO_FILE, OUTPUT_FILE) + + # # 清理临时文件 + # os.remove(TEMP_AUDIO_FILE) + # os.remove(TEMP_VIDEO_FILE) + + print("录制完成！") + + +# -------------- Load QWen2-VL Model ------------ +# default: Load the model on the available device(s) +model = Qwen2VLForConditionalGeneration.from_pretrained( + "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" +) +# ------- 设置分辨率，降低现存占用 ------- +min_pixels = 256*28*28 +max_pixels = 512*28*28 +processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) +# -------------------------------------- + +# -------- SenceVoice 语音识别 --模型加载----- +model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) + +if __name__ == "__main__": + while 1: + main() + folder_path = "./Test_QWen2_VL/" + os.makedirs(folder_path, exist_ok=True) + + file_path = os.path.join(folder_path, "captured_image.jpg") # 设置保存路径 + cap = cv2.VideoCapture(TEMP_VIDEO_FILE) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + frame_index = int(total_frames // 2) + # 设置视频帧位置 + cap.set(cv2.CAP_PROP_POS_FRAMES, frame_index) + ret, frame = cap.read() + if not ret: + print(f"无法读取帧索引 {frame_index}") + else: + # 显示帧 + cv2.imwrite(file_path, frame) + # cv2.imshow(f"Frame {frame_index}", frame) + + # -------- SenceVoice 推理 --------- + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + prompt = res[0]['text'].split(">")[-1] + # ---------SenceVoice --end---------- + + # -------- QWen2-VL 模型推理 --------- + messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": f"{file_path}", + }, + {"type": "text", "text": f"{prompt}"}, + ], + } + ] + + # Preparation for inference + text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + image_inputs, video_inputs = process_vision_info(messages) + inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + inputs = inputs.to("cuda") + + # Inference: Generation of the output + generated_ids = model.generate(**inputs, max_new_tokens=128) + generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + output_text = processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False + ) + print(output_text) + + # 输入文本 + text = output_text[0] + # asyncio.run(amain(text, "zh-CN-YunxiaNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + + asyncio.run(amain(text, "zh-CN-XiaoyiNeural", os.path.join(folder_path,"sft_0.mp3"))) + play_audio(f'{folder_path}/sft_0.mp3') + + # asyncio.run(amain(text, "zh-CN-YunjianNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + + # asyncio.run(amain(text, "zh-CN-shaanxi-XiaoniNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') \ No newline at end of file diff --git a/7.5_realTime_AV.py b/7.5_realTime_AV.py new file mode 100644 index 0000000..650d5c1 --- /dev/null +++ b/7.5_realTime_AV.py @@ -0,0 +1,316 @@ +import cv2 +import pyaudio +import wave +import threading +import numpy as np +import time +from queue import Queue +import webrtcvad +import os +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +import edge_tts +import asyncio +from time import sleep + +# 参数设置 +AUDIO_RATE = 16000 # 音频采样率 +AUDIO_CHANNELS = 1 # 单声道 +CHUNK = 1024 # 音频块大小 +VAD_MODE = 3 # VAD 模式 (0-3, 数字越大越敏感) +OUTPUT_DIR = "./output" # 输出目录 +NO_SPEECH_THRESHOLD = 1 # 无效语音阈值，单位：秒 + +# 确保输出目录存在 +os.makedirs(OUTPUT_DIR, exist_ok=True) + +# 队列用于音频和视频同步缓存 +audio_queue = Queue() +video_queue = Queue() + +# 全局变量 +last_active_time = time.time() +recording_active = True +segments_to_save = [] +saved_intervals = [] +last_vad_end_time = 0 # 上次保存的 VAD 有效段结束时间 + +# 初始化 WebRTC VAD +vad = webrtcvad.Vad() +vad.set_mode(VAD_MODE) + +# 音频录制线程 +def audio_recorder(): + global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time + + p = pyaudio.PyAudio() + stream = p.open(format=pyaudio.paInt16, + channels=AUDIO_CHANNELS, + rate=AUDIO_RATE, + input=True, + frames_per_buffer=CHUNK) + + audio_buffer = [] + print("音频录制已开始") + + while recording_active: + data = stream.read(CHUNK) + audio_buffer.append(data) + + # 每 0.5 秒检测一次 VAD + if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5: + # 拼接音频数据并检测 VAD + raw_audio = b''.join(audio_buffer) + vad_result = check_vad_activity(raw_audio) + + if vad_result: + print("检测到语音活动") + last_active_time = time.time() + segments_to_save.append((raw_audio, time.time())) + else: + print("静音中...") + + audio_buffer = [] # 清空缓冲区 + + # 检查无效语音时间 + if time.time() - last_active_time > NO_SPEECH_THRESHOLD: + # 检查是否需要保存 + if segments_to_save and segments_to_save[-1][1] > last_vad_end_time: + save_audio_video() + last_active_time = time.time() + else: + pass + # print("无新增语音段，跳过保存") + + stream.stop_stream() + stream.close() + p.terminate() + +# 视频录制线程 +def video_recorder(): + global video_queue, recording_active + + cap = cv2.VideoCapture(0) # 使用默认摄像头 + print("视频录制已开始") + + while recording_active: + ret, frame = cap.read() + if ret: + video_queue.put((frame, time.time())) + + # 实时显示摄像头画面 + cv2.imshow("Real Camera", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 键退出 + break + else: + print("无法获取摄像头画面") + + cap.release() + cv2.destroyAllWindows() + +# 检测 VAD 活动 +def check_vad_activity(audio_data): + # 将音频数据分块检测 + num, rate = 0, 0.4 + step = int(AUDIO_RATE * 0.02) # 20ms 块大小 + flag_rate = round(rate * len(audio_data) // step) + + for i in range(0, len(audio_data), step): + chunk = audio_data[i:i + step] + if len(chunk) == step: + if vad.is_speech(chunk, sample_rate=AUDIO_RATE): + num += 1 + + if num > flag_rate: + return True + return False + +# 保存音频和视频 +def save_audio_video(): + global segments_to_save, video_queue, last_vad_end_time, saved_intervals + + if not segments_to_save: + return + + # 获取有效段的时间范围 + start_time = segments_to_save[0][1] + end_time = segments_to_save[-1][1] + + # 检查是否与之前的片段重叠 + if saved_intervals and saved_intervals[-1][1] >= start_time: + print("当前片段与之前片段重叠，跳过保存") + segments_to_save.clear() + return + + # 保存音频 + audio_frames = [seg[0] for seg in segments_to_save] + audio_output_path = f"{OUTPUT_DIR}/audio_0.wav" + wf = wave.open(audio_output_path, 'wb') + wf.setnchannels(AUDIO_CHANNELS) + wf.setsampwidth(2) # 16-bit PCM + wf.setframerate(AUDIO_RATE) + wf.writeframes(b''.join(audio_frames)) + wf.close() + print(f"音频保存至 {audio_output_path}") + + # 保存视频 + video_frames = [] + while not video_queue.empty(): + frame, timestamp = video_queue.get() + if start_time <= timestamp <= end_time: + video_frames.append(frame) + + if video_frames: + video_output_path = f"{OUTPUT_DIR}/video_0.avi" + out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480)) + for frame in video_frames: + out.write(frame) + out.release() + print(f"视频保存至 {video_output_path}") + Inference() + else: + pass + # print("无可保存的视频帧") + + # 记录保存的区间 + saved_intervals.append((start_time, end_time)) + + # 清空缓冲区 + segments_to_save.clear() + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + +# -------------- Load QWen2-VL Model ------------ +# default: Load the model on the available device(s) +model = Qwen2VLForConditionalGeneration.from_pretrained( + "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" +) +# ------- 设置分辨率，降低现存占用 ------- +min_pixels = 256*28*28 +max_pixels = 512*28*28 +processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) +# -------------------------------------- + +# -------- SenceVoice 语音识别 --模型加载----- +model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) +folder_path = "./Test_QWen2_VL/" + +def Inference(TEMP_VIDEO_FILE=f"{OUTPUT_DIR}/video_0.avi", TEMP_AUDIO_FILE=f"{OUTPUT_DIR}/audio_0.wav"): + file_path = os.path.join(folder_path, "captured_image.jpg") # 设置保存路径 + cap = cv2.VideoCapture(TEMP_VIDEO_FILE) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + frame_index = int(total_frames // 2) + # 设置视频帧位置 + cap.set(cv2.CAP_PROP_POS_FRAMES, frame_index) + ret, frame = cap.read() + if not ret: + print(f"无法读取帧索引 {frame_index}") + else: + # 显示帧 + cv2.imwrite(file_path, frame) + # cv2.imshow(f"Frame {frame_index}", frame) + + # -------- SenceVoice 推理 --------- + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + prompt = res[0]['text'].split(">")[-1] + # ---------SenceVoice --end---------- + + # -------- QWen2-VL 模型推理 --------- + messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": f"{file_path}", + }, + {"type": "text", "text": f"{prompt}"}, + ], + } + ] + + # Preparation for inference + text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + image_inputs, video_inputs = process_vision_info(messages) + inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + inputs = inputs.to("cuda") + + # Inference: Generation of the output + generated_ids = model.generate(**inputs, max_new_tokens=128) + generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + output_text = processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False + ) + print(output_text) + + # 输入文本 + text = output_text[0] + # asyncio.run(amain(text, "zh-CN-YunxiaNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + + asyncio.run(amain(text, "zh-CN-XiaoyiNeural", os.path.join(folder_path,"sft_0.mp3"))) + play_audio(f'{folder_path}/sft_0.mp3') + + # asyncio.run(amain(text, "zh-CN-YunjianNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + + # asyncio.run(amain(text, "zh-CN-shaanxi-XiaoniNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + +# 主函数 +if __name__ == "__main__": + try: + # 启动音视频录制线程 + audio_thread = threading.Thread(target=audio_recorder) + video_thread = threading.Thread(target=video_recorder) + audio_thread.start() + video_thread.start() + + print("按 Ctrl+C 停止录制") + while True: + time.sleep(1) + + except KeyboardInterrupt: + print("录制停止中...") + recording_active = False + audio_thread.join() + video_thread.join() + print("录制已停止") diff --git a/7.6_realTime_debug.py b/7.6_realTime_debug.py new file mode 100644 index 0000000..0407778 --- /dev/null +++ b/7.6_realTime_debug.py @@ -0,0 +1,316 @@ +import cv2 +import pyaudio +import wave +import threading +import numpy as np +import time +from queue import Queue +import webrtcvad +import os +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +import edge_tts +import asyncio +from time import sleep + +# 参数设置 +AUDIO_RATE = 16000 # 音频采样率 +AUDIO_CHANNELS = 1 # 单声道 +CHUNK = 1024 # 音频块大小 +VAD_MODE = 3 # VAD 模式 (0-3, 数字越大越敏感) +OUTPUT_DIR = "./output" # 输出目录 +NO_SPEECH_THRESHOLD = 1 # 无效语音阈值，单位：秒 + +# 确保输出目录存在 +os.makedirs(OUTPUT_DIR, exist_ok=True) + +# 队列用于音频和视频同步缓存 +audio_queue = Queue() +video_queue = Queue() + +# 全局变量 +last_active_time = time.time() +recording_active = True +segments_to_save = [] +saved_intervals = [] +last_vad_end_time = 0 # 上次保存的 VAD 有效段结束时间 + +# 初始化 WebRTC VAD +vad = webrtcvad.Vad() +vad.set_mode(VAD_MODE) + +# 音频录制线程 +def audio_recorder(): + global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time + + p = pyaudio.PyAudio() + stream = p.open(format=pyaudio.paInt16, + channels=AUDIO_CHANNELS, + rate=AUDIO_RATE, + input=True, + frames_per_buffer=CHUNK) + + audio_buffer = [] + print("音频录制已开始") + + while recording_active: + data = stream.read(CHUNK) + audio_buffer.append(data) + + # 每 0.5 秒检测一次 VAD + if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5: + # 拼接音频数据并检测 VAD + raw_audio = b''.join(audio_buffer) + vad_result = check_vad_activity(raw_audio) + + if vad_result: + print("检测到语音活动") + last_active_time = time.time() + segments_to_save.append((raw_audio, time.time())) + else: + print("静音中...") + + audio_buffer = [] # 清空缓冲区 + + # 检查无效语音时间 + if time.time() - last_active_time > NO_SPEECH_THRESHOLD: + # 检查是否需要保存 + if segments_to_save and segments_to_save[-1][1] > last_vad_end_time: + save_audio_video() + last_active_time = time.time() + else: + pass + # print("无新增语音段，跳过保存") + + stream.stop_stream() + stream.close() + p.terminate() + +# 视频录制线程 +def video_recorder(): + global video_queue, recording_active + + cap = cv2.VideoCapture(0) # 使用默认摄像头 + print("视频录制已开始") + + while recording_active: + ret, frame = cap.read() + if ret: + video_queue.put((frame, time.time())) + + # 实时显示摄像头画面 + cv2.imshow("Real Camera", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 键退出 + break + else: + print("无法获取摄像头画面") + + cap.release() + cv2.destroyAllWindows() + +# 检测 VAD 活动 +def check_vad_activity(audio_data): + # 将音频数据分块检测 + num = 0 + step = int(AUDIO_RATE * 0.02) # 20ms 块大小 + flag_rate = round(0.8 * len(audio_data) // step) + + for i in range(0, len(audio_data), step): + chunk = audio_data[i:i + step] + if len(chunk) == step: + if vad.is_speech(chunk, sample_rate=AUDIO_RATE): + num += 1 + + if num > flag_rate: + return True + return False + +# 保存音频和视频 +def save_audio_video(): + global segments_to_save, video_queue, last_vad_end_time, saved_intervals + + if not segments_to_save: + return + + # 获取有效段的时间范围 + start_time = segments_to_save[0][1] + end_time = segments_to_save[-1][1] + + # 检查是否与之前的片段重叠 + if saved_intervals and saved_intervals[-1][1] >= start_time: + print("当前片段与之前片段重叠，跳过保存") + segments_to_save.clear() + return + + # 保存音频 + audio_frames = [seg[0] for seg in segments_to_save] + audio_output_path = f"{OUTPUT_DIR}/audio_0.wav" + wf = wave.open(audio_output_path, 'wb') + wf.setnchannels(AUDIO_CHANNELS) + wf.setsampwidth(2) # 16-bit PCM + wf.setframerate(AUDIO_RATE) + wf.writeframes(b''.join(audio_frames)) + wf.close() + print(f"音频保存至 {audio_output_path}") + + # 保存视频 + video_frames = [] + while not video_queue.empty(): + frame, timestamp = video_queue.get() + if start_time <= timestamp <= end_time: + video_frames.append(frame) + + if video_frames: + video_output_path = f"{OUTPUT_DIR}/video_0.avi" + out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480)) + for frame in video_frames: + out.write(frame) + out.release() + print(f"视频保存至 {video_output_path}") + # Inference() + else: + pass + # print("无可保存的视频帧") + + # 记录保存的区间 + saved_intervals.append((start_time, end_time)) + + # 清空缓冲区 + segments_to_save.clear() + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + +# # -------------- Load QWen2-VL Model ------------ +# # default: Load the model on the available device(s) +# model = Qwen2VLForConditionalGeneration.from_pretrained( +# "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" +# ) +# # ------- 设置分辨率，降低现存占用 ------- +# min_pixels = 256*28*28 +# max_pixels = 512*28*28 +# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) +# # -------------------------------------- + +# # -------- SenceVoice 语音识别 --模型加载----- +# model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall" +# model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) +# folder_path = "./Test_QWen2_VL/" + +# def Inference(TEMP_VIDEO_FILE=f"{OUTPUT_DIR}/video_0.avi", TEMP_AUDIO_FILE=f"{OUTPUT_DIR}/audio_0.wav"): +# file_path = os.path.join(folder_path, "captured_image.jpg") # 设置保存路径 +# cap = cv2.VideoCapture(TEMP_VIDEO_FILE) +# total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) +# frame_index = int(total_frames // 2) +# # 设置视频帧位置 +# cap.set(cv2.CAP_PROP_POS_FRAMES, frame_index) +# ret, frame = cap.read() +# if not ret: +# print(f"无法读取帧索引 {frame_index}") +# else: +# # 显示帧 +# cv2.imwrite(file_path, frame) +# # cv2.imshow(f"Frame {frame_index}", frame) + +# # -------- SenceVoice 推理 --------- +# input_file = (TEMP_AUDIO_FILE) +# res = model_senceVoice.generate( +# input=input_file, +# cache={}, +# language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" +# use_itn=False, +# ) +# prompt = res[0]['text'].split(">")[-1] +# # ---------SenceVoice --end---------- + +# # -------- QWen2-VL 模型推理 --------- +# messages = [ +# { +# "role": "user", +# "content": [ +# { +# "type": "image", +# "image": f"{file_path}", +# }, +# {"type": "text", "text": f"{prompt}"}, +# ], +# } +# ] + +# # Preparation for inference +# text = processor.apply_chat_template( +# messages, tokenize=False, add_generation_prompt=True +# ) +# image_inputs, video_inputs = process_vision_info(messages) +# inputs = processor( +# text=[text], +# images=image_inputs, +# videos=video_inputs, +# padding=True, +# return_tensors="pt", +# ) +# inputs = inputs.to("cuda") + +# # Inference: Generation of the output +# generated_ids = model.generate(**inputs, max_new_tokens=128) +# generated_ids_trimmed = [ +# out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) +# ] +# output_text = processor.batch_decode( +# generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False +# ) +# print(output_text) + +# # 输入文本 +# text = output_text[0] +# # asyncio.run(amain(text, "zh-CN-YunxiaNeural", os.path.join(folder_path,"sft_0.mp3"))) +# # play_audio(f'{folder_path}/sft_0.mp3') + +# asyncio.run(amain(text, "zh-CN-XiaoyiNeural", os.path.join(folder_path,"sft_0.mp3"))) +# play_audio(f'{folder_path}/sft_0.mp3') + +# # asyncio.run(amain(text, "zh-CN-YunjianNeural", os.path.join(folder_path,"sft_0.mp3"))) +# # play_audio(f'{folder_path}/sft_0.mp3') + +# # asyncio.run(amain(text, "zh-CN-shaanxi-XiaoniNeural", os.path.join(folder_path,"sft_0.mp3"))) +# # play_audio(f'{folder_path}/sft_0.mp3') + +# 主函数 +if __name__ == "__main__": + try: + # 启动音视频录制线程 + audio_thread = threading.Thread(target=audio_recorder) + video_thread = threading.Thread(target=video_recorder) + audio_thread.start() + video_thread.start() + + print("按 Ctrl+C 停止录制") + while True: + time.sleep(1) + + except KeyboardInterrupt: + print("录制停止中...") + recording_active = False + audio_thread.join() + video_thread.join() + print("录制已停止") diff --git a/7.7_realTime_AV_multiImage.py b/7.7_realTime_AV_multiImage.py new file mode 100644 index 0000000..b344c8e --- /dev/null +++ b/7.7_realTime_AV_multiImage.py @@ -0,0 +1,319 @@ +import cv2 +import pyaudio +import wave +import threading +import numpy as np +import time +from queue import Queue +import webrtcvad +import os +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +import edge_tts +import asyncio +from time import sleep + +# 参数设置 +AUDIO_RATE = 16000 # 音频采样率 +AUDIO_CHANNELS = 1 # 单声道 +CHUNK = 1024 # 音频块大小 +VAD_MODE = 3 # VAD 模式 (0-3, 数字越大越敏感) +OUTPUT_DIR = "./output" # 输出目录 +NO_SPEECH_THRESHOLD = 1 # 无效语音阈值，单位：秒 + +# 确保输出目录存在 +os.makedirs(OUTPUT_DIR, exist_ok=True) + +# 队列用于音频和视频同步缓存 +audio_queue = Queue() +video_queue = Queue() + +# 全局变量 +last_active_time = time.time() +recording_active = True +segments_to_save = [] +saved_intervals = [] +last_vad_end_time = 0 # 上次保存的 VAD 有效段结束时间 + +# 初始化 WebRTC VAD +vad = webrtcvad.Vad() +vad.set_mode(VAD_MODE) + +# 音频录制线程 +def audio_recorder(): + global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time + + p = pyaudio.PyAudio() + stream = p.open(format=pyaudio.paInt16, + channels=AUDIO_CHANNELS, + rate=AUDIO_RATE, + input=True, + frames_per_buffer=CHUNK) + + audio_buffer = [] + print("音频录制已开始") + + while recording_active: + data = stream.read(CHUNK) + audio_buffer.append(data) + + # 每 0.5 秒检测一次 VAD + if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5: + # 拼接音频数据并检测 VAD + raw_audio = b''.join(audio_buffer) + vad_result = check_vad_activity(raw_audio) + + if vad_result: + print("检测到语音活动") + last_active_time = time.time() + segments_to_save.append((raw_audio, time.time())) + else: + print("静音中...") + + audio_buffer = [] # 清空缓冲区 + + # 检查无效语音时间 + if time.time() - last_active_time > NO_SPEECH_THRESHOLD: + # 检查是否需要保存 + if segments_to_save and segments_to_save[-1][1] > last_vad_end_time: + save_audio_video() + last_active_time = time.time() + else: + pass + # print("无新增语音段，跳过保存") + + stream.stop_stream() + stream.close() + p.terminate() + +# 视频录制线程 +def video_recorder(): + global video_queue, recording_active + + cap = cv2.VideoCapture(0) # 使用默认摄像头 + print("视频录制已开始") + + while recording_active: + ret, frame = cap.read() + if ret: + video_queue.put((frame, time.time())) + + # 实时显示摄像头画面 + cv2.imshow("Real Camera", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 键退出 + break + else: + print("无法获取摄像头画面") + + cap.release() + cv2.destroyAllWindows() + +# 检测 VAD 活动 +def check_vad_activity(audio_data): + # 将音频数据分块检测 + num, rate = 0, 0.4 + step = int(AUDIO_RATE * 0.02) # 20ms 块大小 + flag_rate = round(rate * len(audio_data) // step) + + for i in range(0, len(audio_data), step): + chunk = audio_data[i:i + step] + if len(chunk) == step: + if vad.is_speech(chunk, sample_rate=AUDIO_RATE): + num += 1 + + if num > flag_rate: + return True + return False + +# 保存音频和视频 +def save_audio_video(): + global segments_to_save, video_queue, last_vad_end_time, saved_intervals + + if not segments_to_save: + return + + # 获取有效段的时间范围 + start_time = segments_to_save[0][1] + end_time = segments_to_save[-1][1] + + # 检查是否与之前的片段重叠 + if saved_intervals and saved_intervals[-1][1] >= start_time: + print("当前片段与之前片段重叠，跳过保存") + segments_to_save.clear() + return + + # 保存音频 + audio_frames = [seg[0] for seg in segments_to_save] + audio_output_path = f"{OUTPUT_DIR}/audio_0.wav" + wf = wave.open(audio_output_path, 'wb') + wf.setnchannels(AUDIO_CHANNELS) + wf.setsampwidth(2) # 16-bit PCM + wf.setframerate(AUDIO_RATE) + wf.writeframes(b''.join(audio_frames)) + wf.close() + print(f"音频保存至 {audio_output_path}") + + # 保存视频 + video_frames = [] + while not video_queue.empty(): + frame, timestamp = video_queue.get() + if start_time <= timestamp <= end_time: + video_frames.append(frame) + + if video_frames: + video_output_path = f"{OUTPUT_DIR}/video_0.avi" + out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480)) + for frame in video_frames: + out.write(frame) + out.release() + print(f"视频保存至 {video_output_path}") + Inference() + else: + pass + # print("无可保存的视频帧") + + # 记录保存的区间 + saved_intervals.append((start_time, end_time)) + + # 清空缓冲区 + segments_to_save.clear() + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + +# -------------- Load QWen2-VL Model ------------ +# default: Load the model on the available device(s) +model = Qwen2VLForConditionalGeneration.from_pretrained( + "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" +) +# ------- 设置分辨率，降低现存占用 ------- +min_pixels = 256*28*28 +max_pixels = 512*28*28 +processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) +# -------------------------------------- + +# -------- SenceVoice 语音识别 --模型加载----- +model_dir = r"E:\2_PYTHON\Project\GPT\QWen\pretrained_models\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) +folder_path = "./Test_QWen2_VL/" + +def Inference(TEMP_VIDEO_FILE=f"{OUTPUT_DIR}/video_0.avi", TEMP_AUDIO_FILE=f"{OUTPUT_DIR}/audio_0.wav"): + + cap = cv2.VideoCapture(TEMP_VIDEO_FILE) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + S_index = [0.2, 0.4, 0.6, 0.8] + frame_index = [int(total_frames * i) for i in S_index] + # 设置视频帧位置 + for idx in frame_index: + cap.set(cv2.CAP_PROP_POS_FRAMES, idx) + ret, frame = cap.read() + if not ret: + print(f"无法读取帧索引 {idx}") + else: + # 保存帧 + file_path = os.path.join(folder_path, f"captured_image{idx}.jpg") # 设置保存路径 + cv2.imwrite(file_path, frame) + + # -------- SenceVoice 推理 --------- + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + prompt = res[0]['text'].split(">")[-1] + print("ASR OUT:", prompt) + # ---------SenceVoice --end---------- + + # -------- QWen2-VL 模型推理 --------- + messages = [ + { + "role": "user", + "content": [ + {"type": "image", "image": f'{os.path.join(folder_path, f"captured_image{frame_index[0]}.jpg")}'}, + {"type": "image", "image": f'{os.path.join(folder_path, f"captured_image{frame_index[1]}.jpg")}'}, + # {"type": "image", "image": f'{os.path.join(folder_path, f"captured_image{frame_index[2]}.jpg")}'}, + # {"type": "image", "image": f'{os.path.join(folder_path, f"captured_image{frame_index[3]}.jpg")}'}, + {"type": "text", "text": f"{prompt}"}, + ], + } + ] + + # Preparation for inference + text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + image_inputs, video_inputs = process_vision_info(messages) + inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + inputs = inputs.to("cuda") + + # Inference + generated_ids = model.generate(**inputs, max_new_tokens=128) + generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + output_text = processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False + ) + print(output_text) + + # 输入文本 + text = output_text[0] + # asyncio.run(amain(text, "zh-CN-YunxiaNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + + asyncio.run(amain(text, "zh-CN-XiaoyiNeural", os.path.join(folder_path,"sft_0.mp3"))) + play_audio(f'{folder_path}/sft_0.mp3') + + # asyncio.run(amain(text, "zh-CN-YunjianNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + + # asyncio.run(amain(text, "zh-CN-shaanxi-XiaoniNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + +# 主函数 +if __name__ == "__main__": + try: + # 启动音视频录制线程 + audio_thread = threading.Thread(target=audio_recorder) + video_thread = threading.Thread(target=video_recorder) + audio_thread.start() + video_thread.start() + + print("按 Ctrl+C 停止录制") + while True: + time.sleep(1) + + except KeyboardInterrupt: + print("录制停止中...") + recording_active = False + audio_thread.join() + video_thread.join() + print("录制已停止") diff --git a/7.8_realTime_AV_video.py b/7.8_realTime_AV_video.py new file mode 100644 index 0000000..cb2818f --- /dev/null +++ b/7.8_realTime_AV_video.py @@ -0,0 +1,342 @@ +import cv2 +import pyaudio +import wave +import threading +import numpy as np +import time +from queue import Queue +import webrtcvad +import os +import threading +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info +import torch +from funasr import AutoModel +import pygame +import edge_tts +import asyncio +from time import sleep + +# 参数设置 +AUDIO_RATE = 16000 # 音频采样率 +AUDIO_CHANNELS = 1 # 单声道 +CHUNK = 1024 # 音频块大小 +VAD_MODE = 3 # VAD 模式 (0-3, 数字越大越敏感) +OUTPUT_DIR = "./output" # 输出目录 +NO_SPEECH_THRESHOLD = 1 # 无效语音阈值，单位：秒 + +# 确保输出目录存在 +os.makedirs(OUTPUT_DIR, exist_ok=True) + +# 队列用于音频和视频同步缓存 +audio_queue = Queue() +video_queue = Queue() + +# 全局变量 +last_active_time = time.time() +recording_active = True +segments_to_save = [] +saved_intervals = [] +last_vad_end_time = 0 # 上次保存的 VAD 有效段结束时间 + +# 初始化 WebRTC VAD +vad = webrtcvad.Vad() +vad.set_mode(VAD_MODE) + +# 音频录制线程 +def audio_recorder(): + global audio_queue, recording_active, last_active_time, segments_to_save, last_vad_end_time + + p = pyaudio.PyAudio() + stream = p.open(format=pyaudio.paInt16, + channels=AUDIO_CHANNELS, + rate=AUDIO_RATE, + input=True, + frames_per_buffer=CHUNK) + + audio_buffer = [] + print("音频录制已开始") + + while recording_active: + data = stream.read(CHUNK) + audio_buffer.append(data) + + # 每 0.5 秒检测一次 VAD + if len(audio_buffer) * CHUNK / AUDIO_RATE >= 0.5: + # 拼接音频数据并检测 VAD + raw_audio = b''.join(audio_buffer) + vad_result = check_vad_activity(raw_audio) + + if vad_result: + print("检测到语音活动") + last_active_time = time.time() + segments_to_save.append((raw_audio, time.time())) + else: + print("静音中...") + + audio_buffer = [] # 清空缓冲区 + + # 检查无效语音时间 + if time.time() - last_active_time > NO_SPEECH_THRESHOLD: + # 检查是否需要保存 + if segments_to_save and segments_to_save[-1][1] > last_vad_end_time: + save_audio_video() + last_active_time = time.time() + else: + pass + # print("无新增语音段，跳过保存") + + stream.stop_stream() + stream.close() + p.terminate() + +# 视频录制线程 +def video_recorder(): + global video_queue, recording_active + + cap = cv2.VideoCapture(0) # 使用默认摄像头 + print("视频录制已开始") + + while recording_active: + ret, frame = cap.read() + if ret: + video_queue.put((frame, time.time())) + + # 实时显示摄像头画面 + cv2.imshow("Real Camera", frame) + if cv2.waitKey(1) & 0xFF == ord('q'): # 按 Q 键退出 + break + else: + print("无法获取摄像头画面") + + cap.release() + cv2.destroyAllWindows() + +# 检测 VAD 活动 +def check_vad_activity(audio_data): + # 将音频数据分块检测 + num, rate = 0, 0.4 + step = int(AUDIO_RATE * 0.02) # 20ms 块大小 + flag_rate = round(rate * len(audio_data) // step) + + for i in range(0, len(audio_data), step): + chunk = audio_data[i:i + step] + if len(chunk) == step: + if vad.is_speech(chunk, sample_rate=AUDIO_RATE): + num += 1 + + if num > flag_rate: + return True + return False + +# 保存音频和视频 +def save_audio_video(): + global segments_to_save, video_queue, last_vad_end_time, saved_intervals + + if not segments_to_save: + return + + # 获取有效段的时间范围 + start_time = segments_to_save[0][1] + end_time = segments_to_save[-1][1] + + # 检查是否与之前的片段重叠 + if saved_intervals and saved_intervals[-1][1] >= start_time: + print("当前片段与之前片段重叠，跳过保存") + segments_to_save.clear() + return + + # 保存音频 + audio_frames = [seg[0] for seg in segments_to_save] + audio_output_path = f"{OUTPUT_DIR}/audio_0.wav" + wf = wave.open(audio_output_path, 'wb') + wf.setnchannels(AUDIO_CHANNELS) + wf.setsampwidth(2) # 16-bit PCM + wf.setframerate(AUDIO_RATE) + wf.writeframes(b''.join(audio_frames)) + wf.close() + print(f"音频保存至 {audio_output_path}") + + # 保存视频 + video_frames = [] + while not video_queue.empty(): + frame, timestamp = video_queue.get() + if start_time <= timestamp <= end_time: + video_frames.append(frame) + + if video_frames: + video_output_path = f"{OUTPUT_DIR}/video_0.avi" + out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*'XVID'), 20.0, (640, 480)) + for frame in video_frames: + out.write(frame) + out.release() + print(f"视频保存至 {video_output_path}") + Inference() + else: + pass + # print("无可保存的视频帧") + + # 记录保存的区间 + saved_intervals.append((start_time, end_time)) + + # 清空缓冲区 + segments_to_save.clear() + +# --- 播放音频 - +def play_audio(file_path): + try: + pygame.mixer.init() + pygame.mixer.music.load(file_path) + pygame.mixer.music.play() + while pygame.mixer.music.get_busy(): + time.sleep(1) # 等待音频播放结束 + print("播放完成！") + except Exception as e: + print(f"播放失败: {e}") + finally: + pygame.mixer.quit() + +async def amain(TEXT, VOICE, OUTPUT_FILE) -> None: + """Main function""" + communicate = edge_tts.Communicate(TEXT, VOICE) + await communicate.save(OUTPUT_FILE) + +# -------------- Load QWen2-VL Model ------------ +# default: Load the model on the available device(s) +model = Qwen2VLForConditionalGeneration.from_pretrained( + "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" +) +# ------- 设置分辨率，降低现存占用 ------- +min_pixels = 256*28*28 +max_pixels = 512*28*28 +processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) +# -------------------------------------- + +# -------- SenceVoice 语音识别 --模型加载----- +model_dir = r"D:\AI\download\SenseVoiceSmall" +model_senceVoice = AutoModel( model=model_dir, trust_remote_code=True, ) +folder_path = "./Test_QWen2_VL/" + +def Inference(TEMP_VIDEO_FILE=f"{OUTPUT_DIR}/video_0.avi", TEMP_AUDIO_FILE=f"{OUTPUT_DIR}/audio_0.wav"): + + cap = cv2.VideoCapture(TEMP_VIDEO_FILE) + total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) + S_index = [0.2, 0.4, 0.6, 0.8] + frame_index = [int(total_frames * i) for i in S_index] + # 设置视频帧位置 + for idx in frame_index: + cap.set(cv2.CAP_PROP_POS_FRAMES, idx) + ret, frame = cap.read() + if not ret: + print(f"无法读取帧索引 {idx}") + else: + # 保存帧 + file_path = os.path.join(folder_path, f"captured_image{idx}.jpg") # 设置保存路径 + cv2.imwrite(file_path, frame) + + # -------- SenceVoice 推理 --------- + input_file = (TEMP_AUDIO_FILE) + res = model_senceVoice.generate( + input=input_file, + cache={}, + language="auto", # "zn", "en", "yue", "ja", "ko", "nospeech" + use_itn=False, + ) + prompt = res[0]['text'].split(">")[-1] + print("ASR OUT:", prompt) + # ---------SenceVoice --end---------- + + # -------- QWen2-VL 模型推理 --------- + # Messages containing a images list as a video and a text query + # messages = [ + # { + # "role": "user", + # "content": [ + # { + # "type": "video", + # "video": [ + # f'{os.path.join(folder_path, f"captured_image{frame_index[0]}.jpg")}', + # f'{os.path.join(folder_path, f"captured_image{frame_index[1]}.jpg")}', + # f'{os.path.join(folder_path, f"captured_image{frame_index[2]}.jpg")}', + # f'{os.path.join(folder_path, f"captured_image{frame_index[3]}.jpg")}', + # ], + # "fps": 1.0, + # }, + # {"type": "text", "text": f"{prompt},同时描述这段视频"}, + # ], + # } + # ] + + # Messages containing a video and a text query + messages = [ + { + "role": "user", + "content": [ + { + "type": "video", + "video": f"{OUTPUT_DIR}/video_0.avi", + "max_pixels": 360 * 420, + "fps": 1.0, + }, + {"type": "text", "text": f"{prompt}"}, + ], + } + ] + + # Preparation for inference + text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + image_inputs, video_inputs = process_vision_info(messages) + inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", + ) + inputs = inputs.to("cuda") + + # Inference + generated_ids = model.generate(**inputs, max_new_tokens=128) + generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) + ] + output_text = processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False + ) + print(output_text) + + # 输入文本 + text = output_text[0] + # asyncio.run(amain(text, "zh-CN-YunxiaNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + + asyncio.run(amain(text, "zh-CN-XiaoyiNeural", os.path.join(folder_path,"sft_0.mp3"))) + play_audio(f'{folder_path}/sft_0.mp3') + + # asyncio.run(amain(text, "zh-CN-YunjianNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + + # asyncio.run(amain(text, "zh-CN-shaanxi-XiaoniNeural", os.path.join(folder_path,"sft_0.mp3"))) + # play_audio(f'{folder_path}/sft_0.mp3') + +# 主函数 +if __name__ == "__main__": + try: + # 启动音视频录制线程 + audio_thread = threading.Thread(target=audio_recorder) + video_thread = threading.Thread(target=video_recorder) + audio_thread.start() + video_thread.start() + + print("按 Ctrl+C 停止录制") + while True: + time.sleep(1) + + except KeyboardInterrupt: + print("录制停止中...") + recording_active = False + audio_thread.join() + video_thread.join() + print("录制已停止") diff --git a/7_0_FunASR.py b/7_0_FunASR.py new file mode 100644 index 0000000..f00c0cc --- /dev/null +++ b/7_0_FunASR.py @@ -0,0 +1,28 @@ + +import torchaudio +from funasr import AutoModel +from IPython.display import Audio + +speaker1_wav = r'E:\2_PYTHON\Project\GPT\QWen\CosyVoice\output\audio_0.wav' +waveform, sample_rate = torchaudio.load(speaker1_wav) +Audio(waveform, rate=sample_rate, autoplay=True) + +# VAD检测 +from funasr import AutoModel +model = AutoModel(model="fsmn-vad") +res = model.generate(input=speaker1_wav) +print(res) + + +# # 多说话人语音识别 +# funasr_model = AutoModel(model="iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", +# vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", +# punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", +# spk_model="damo/speech_campplus_sv_zh-cn_16k-common", +# ) +# res = funasr_model.generate(input=f"multi_speaker.wav", +# batch_size_s=300) +# print(res[0]['text']) +# res_srt = generate_srt(res[0]['sentence_info']) +# print(res_srt) + diff --git a/7_Inference_QWen2-VL.py b/7_Inference_QWen2-VL.py new file mode 100644 index 0000000..9a0f419 --- /dev/null +++ b/7_Inference_QWen2-VL.py @@ -0,0 +1,61 @@ +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info +import torch + +# default: Load the model on the available device(s) +model = Qwen2VLForConditionalGeneration.from_pretrained( + "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" +) + +# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios. +# model = Qwen2VLForConditionalGeneration.from_pretrained( +# "Qwen/Qwen2-VL-2B-Instruct", +# torch_dtype=torch.bfloat16, +# attn_implementation="flash_attention_2", +# device_map="auto", +# ) + +# default processer +# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct") + +# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage. +min_pixels = 256*28*28 +max_pixels = 1280*28*28 +processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) + +messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", + }, + {"type": "text", "text": "Describe this image."}, + ], + } +] + +# Preparation for inference +text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True +) +image_inputs, video_inputs = process_vision_info(messages) +inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", +) +inputs = inputs.to("cuda") + +# Inference: Generation of the output +generated_ids = model.generate(**inputs, max_new_tokens=128) +generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) +] +output_text = processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False +) +print(output_text) diff --git a/8_Inference_QWen2-VL_offline_AV.py b/8_Inference_QWen2-VL_offline_AV.py new file mode 100644 index 0000000..05def63 --- /dev/null +++ b/8_Inference_QWen2-VL_offline_AV.py @@ -0,0 +1,50 @@ +from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor +from qwen_vl_utils import process_vision_info +import torch + +# default: Load the model on the available device(s) +model = Qwen2VLForConditionalGeneration.from_pretrained( + "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto" +) + +# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage. +min_pixels = 256*28*28 +max_pixels = 1280*28*28 +processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels) + +messages = [ + { + "role": "user", + "content": [ + { + "type": "image", + "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg", + }, + {"type": "text", "text": "Describe this image."}, + ], + } +] + +# Preparation for inference +text = processor.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True +) +image_inputs, video_inputs = process_vision_info(messages) +inputs = processor( + text=[text], + images=image_inputs, + videos=video_inputs, + padding=True, + return_tensors="pt", +) +inputs = inputs.to("cuda") + +# Inference: Generation of the output +generated_ids = model.generate(**inputs, max_new_tokens=128) +generated_ids_trimmed = [ + out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) +] +output_text = processor.batch_decode( + generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False +) +print(output_text) diff --git a/9.1_test_cam++.py b/9.1_test_cam++.py new file mode 100644 index 0000000..e4229c1 --- /dev/null +++ b/9.1_test_cam++.py @@ -0,0 +1,27 @@ +# --- 声纹识别模型 CAM++测试 3D-Speaker数据训练 --- +from modelscope.pipelines import pipeline + +sv_pipeline = pipeline( + task='speaker-verification', + model='damo/speech_campplus_sv_zh-cn_16k-common', + model_revision='v1.0.0' +) + +speaker1_a_wav = r'E:\2_PYTHON\Project\GPT\QWen\CosyVoice\SpeakerVerification_DIR\enroll_wav\enroll_0.wav' +speaker1_b_wav = r'E:\2_PYTHON\Project\GPT\QWen\CosyVoice\SpeakerVerification_DIR\enroll_wav\enroll_1.wav' +speaker2_a_wav = 'https://modelscope.cn/api/v1/models/damo/speech_campplus_sv_zh-cn_16k-common/repo?Revision=master&FilePath=examples/speaker2_a_cn_16k.wav' +# 相同说话人语音 +result = sv_pipeline([speaker1_a_wav, speaker1_b_wav]) +print(result) +# 不同说话人语音 +result = sv_pipeline([speaker1_a_wav, speaker2_a_wav]) +print(result) +# 可以自定义得分阈值来进行识别，阈值越高，判定为同一人的条件越严格 +result = sv_pipeline([speaker1_a_wav, speaker2_a_wav], thr=0.31) +print(result) +# 可以传入output_emb参数，输出结果中就会包含提取到的说话人embedding +result = sv_pipeline([speaker1_a_wav, speaker2_a_wav], output_emb=True) +print(result['embs'], result['outputs']) +# 可以传入save_dir参数，提取到的说话人embedding会存储在save_dir目录中 +save_path = r"E:\2_PYTHON\Project\GPT\QWen\CosyVoice\SpeakerVerification_DIR\enroll_ivec" +result = sv_pipeline([speaker1_a_wav, speaker2_a_wav], save_dir=save_path) diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..66c2a4c --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at mikelei@mobvoi.com. All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/CosyVoice1.py b/CosyVoice1.py new file mode 100644 index 0000000..8368c84 --- /dev/null +++ b/CosyVoice1.py @@ -0,0 +1,8 @@ +from cosyvoice.cli.cosyvoice import CosyVoice +from cosyvoice.utils.file_utils import load_wav +import torchaudio +cosyvoice = CosyVoice(r'D:\AI\download\CosyVoice-300M', load_jit=True, load_onnx=False, fp16=True) +print(cosyvoice.list_avaliable_spks()) +prompt_speech_16k = load_wav('example_audio.wav', 16000) +for i, j in enumerate(cosyvoice.inference_zero_shot('你好，我是通义生成式语音大模型，请问有什么可以帮您的吗？', '对，这就是我，万人敬仰的太乙真人', prompt_speech_16k, stream=False)): + torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], 22050) \ No newline at end of file diff --git a/CosyVoice2.py b/CosyVoice2.py new file mode 100644 index 0000000..5741ef1 --- /dev/null +++ b/CosyVoice2.py @@ -0,0 +1,31 @@ +import sys +sys.path.append('third_party/Matcha-TTS') +from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2 +from cosyvoice.utils.file_utils import load_wav +import torchaudio + +cosyvoice = CosyVoice2('pretrained_models/CosyVoice2-0.5B', load_jit=False, load_trt=False, fp16=False) + +# NOTE if you want to reproduce the results on https://funaudiollm.github.io/cosyvoice2, please add text_frontend=False during inference +# zero_shot usage +prompt_speech_16k = load_wav('./asset/zero_shot_prompt.wav', 16000) +for i, j in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=False)): + torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + +# fine grained control, for supported control, check cosyvoice/tokenizer/tokenizer.py#L248 +for i, j in enumerate(cosyvoice.inference_cross_lingual('在他讲述那个荒诞故事的过程中，他突然[laughter]停下来，因为他自己也被逗笑了[laughter]。', prompt_speech_16k, stream=False)): + torchaudio.save('fine_grained_control_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + +# instruct usage +for i, j in enumerate(cosyvoice.inference_instruct2('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '用四川话说这句话', prompt_speech_16k, stream=False)): + torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) + +# bistream usage, you can use generator as input, this is useful when using text llm model as input +# NOTE you should still have some basic sentence split logic because llm can not handle arbitrary sentence length +def text_generator(): + yield '收到好友从远方寄来的生日礼物，' + yield '那份意外的惊喜与深深的祝福' + yield '让我心中充满了甜蜜的快乐，' + yield '笑容如花儿般绽放。' +for i, j in enumerate(cosyvoice.inference_zero_shot(text_generator(), '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=False)): + torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], cosyvoice.sample_rate) \ No newline at end of file diff --git a/FAQ.md b/FAQ.md new file mode 100644 index 0000000..53df058 --- /dev/null +++ b/FAQ.md @@ -0,0 +1,16 @@ +## ModuleNotFoundError: No module named 'matcha' + +Matcha-TTS is a third_party module. Please check `third_party` directory. If there is no `Matcha-TTS`, execute `git submodule update --init --recursive`. + +run `export PYTHONPATH=third_party/Matcha-TTS` if you want to use `from cosyvoice.cli.cosyvoice import CosyVoice` in python script. + +## cannot find resource.zip or cannot unzip resource.zip + +Please make sure you have git-lfs installed. Execute + +```sh +git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrd +cd pretrained_models/CosyVoice-ttsfrd/ +unzip resource.zip -d . +pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whl +``` diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/README.md b/README.md new file mode 100644 index 0000000..d719002 --- /dev/null +++ b/README.md @@ -0,0 +1,119 @@ +# 环境配置详细教程 [B站](https://www.bilibili.com/video/BV1HucueQEJo/) + +0. anaconda\ffmpeg安装 +``` + 网上很多教程，自行搜索 +``` + +``` + SenseVoiceSmall模型下载： + 自动下载：设置215行 model_dir = "iic/SenseVoiceSmall" + 手动下载：https://www.modelscope.cn/models/iic/SenseVoiceSmall/files + + QWen模型下载： + 自动下载：设置220行 model_name = "Qwen/Qwen2.5-1.5B-Instruct"，开启科学上网，可从huggingface自动下载 + 手动下载：https://www.modelscope.cn/models/ 搜索QWen，结果中下载显存可支持模型 +``` + +1. 创建虚拟环境 +``` + conda create -n chatAudio python=3.10 + conda activate chatAudio +``` +2. 安装pytorch+cuda版本，本地测试2.0以上版本均可，这里安装torch=2.3.1+cuda11.8 +``` + pip install torch==2.3.1 torchvision==0.18.1 torchaudio==2.3.1 --index-url https://download.pytorch.org/whl/cu118 + + 其它适合自己电脑的torch+cuda版本可在torch官网查找 + https://pytorch.org/get-started/previous-versions/ +``` + +3. 简易版本安装，不使用cosyvoice时依赖项较少 +``` + pip install edge-tts==6.1.17 funasr==1.1.12 ffmpeg==1.4 opencv-python==4.10.0.84 transformers==4.45.2 webrtcvad==2.0.10 qwen-vl-utils==0.0.8 pygame==2.6.1 langid==1.1.6 langdetect==1.0.9 accelerate==0.33.0 PyAudio==0.2.14 + + 可执行验证： + python 13_SenceVoice_QWen2.5_edgeTTS_realTime.py +``` + +至此，不调用cosyvoice作为合成的交互可成功调用了。 + +4. cosyvoice依赖库 +``` + 大家反馈较多pynini、wetext安装方法： + conda install -c conda-forge pynini=2.1.6 + pip install WeTextProcessing --no-deps +``` + +5. cosyvoice其它依赖项安装（如遇到权限问题导致安装失败，以管理员形式打开终端） +``` + pip install HyperPyYAML==1.2.2 modelscope==1.15.0 onnxruntime==1.19.2 openai-whisper==20231117 importlib_resources==6.4.5 sounddevice==0.5.1 matcha-tts==0.0.7.0 + + 可执行验证： + python 10_SenceVoice_QWen2.5_cosyVoice.py +``` + +# :sparkles: 241130-updata + +## 新增声纹识别功能 + +设置固定声纹注册语音存储目录，如目录为空则自动进入声纹注册模式。默认注册语音时长大于3秒，可自定义，一般而言时长越长，声纹效果越稳定。 +声纹模型采用阿里开源的CAM++，其采用3D-Speaker中文数据训练，符合中文对话需求 + +## 新增自由定义唤醒词功能 + +使用SenceVoice的语音识别能力实现，将语音识别的汉字转为拼音进行匹配。将唤醒词/指令词设置为中文对应拼音，可自由定制。15.0_SenceVoice_kws_CAM++.py中默认为'ni hao xiao qian'，15.1_SenceVoice_kws_CAM++.py中默认为'zhan qi lai'[暗影君王实在太cool辣] + +## 新增对话历史内容记忆功能 + +通过建立user、system历史队列实现。开启新一轮对话时，首先获取历史记忆，而后拼接新的输入指令。可自由定义最大历史长度，默认为512。 + +对应脚本： + +无历史记忆：15.0_SenceVoice_kws_CAM++.py + +有历史记忆：15.1_SenceVoice_kws_CAM++.py + +[演示demo，B站] (https://www.bilibili.com/video/BV1Q6zpYpEgv) + +Have fun! 😊 + +# :sparkles: 241123-updata + +## 更新单模态自由打断语音交互 + +使用webrtcvad进行实时vad检测，设置一个检测时间段=0.5s，有效语音激活率=40%，每个检测chunk=20ms。也就是说500ms/20ms=25个检测段，如果25*0.4=10个片段激活，则该0.5秒为有效音，加入缓存。 + +可改进点：使用模型VAD，去除噪声干扰 + +13_SenceVoice_QWen2.5_edgeTTS_realTime.py + +## 音视频多模态语音交互 + +基于以上逻辑，替换QWen2.5-1.5B模型为QWen2-VL-2B，可实现音视频多模态交互。模型具有两种输入格式，图片/视频 + +14_SenceVoice_QWen2VL_edgeTTS_realTime.py + +[演示demo，B站] (https://www.bilibili.com/video/BV1uQBCYrEYL) + +# :sparkles: 241027-语音交互大模型/SenceVoice-QWen2.5-TTS + +## 框架 + +SenceVoice-QWen2.5-CosyVoice搭建 + +此工程主代码来于[CosyVoice] (https://github.com/FunAudioLLM/CosyVoice) + +在CosyVoice基础上添加[SenceVoice] (https://github.com/modelscope/FunASR) 作为语音识别模型 + +添加[QWwn2.5] (https://github.com/QwenLM/Qwen2.5) 作为大语言模型进行对话理解 + +## 3种语音合成方法 + +CoosyVoice推理速度慢，严重影响对话实时性，额外添加pyttsx3和edgeTTS + +EdgeTTS实验过程出现链接错误问题，升级版本至6.1.17解决，无需科学上网 + +All dependencies are listed in requirements.txt, the interactive inference scripts are 10/11/12_SenceVoice_QWen2.5_xxx.py. + +Have fun! 😊 diff --git a/README_CosyVoice.md b/README_CosyVoice.md new file mode 100644 index 0000000..0e01098 --- /dev/null +++ b/README_CosyVoice.md @@ -0,0 +1,193 @@ +# CosyVoice +## 👉🏻 [CosyVoice Demos](https://fun-audio-llm.github.io/) 👈🏻 +[[CosyVoice Paper](https://fun-audio-llm.github.io/pdf/CosyVoice_v1.pdf)][[CosyVoice Studio](https://www.modelscope.cn/studios/iic/CosyVoice-300M)][[CosyVoice Code](https://github.com/FunAudioLLM/CosyVoice)] + +For `SenseVoice`, visit [SenseVoice repo](https://github.com/FunAudioLLM/SenseVoice) and [SenseVoice space](https://www.modelscope.cn/studios/iic/SenseVoice). + +## Roadmap + +- [x] 2024/07 + + - [x] Flow matching training support + - [x] WeTextProcessing support when ttsfrd is not avaliable + - [x] Fastapi server and client + +- [x] 2024/08 + + - [x] Repetition Aware Sampling(RAS) inference for llm stability + - [x] Streaming inference mode support, including kv cache and sdpa for rtf optimization + +- [x] 2024/09 + + - [x] 25hz cosyvoice base model + - [x] 25hz cosyvoice voice conversion model + +- [ ] TBD + + - [ ] 25hz llama based llm model which supports lora finetune + - [ ] Support more instruction mode + - [ ] Music generation + - [ ] CosyVoice-500M trained with more multi-lingual data + - [ ] More... + +## Install + +**Clone and install** + +- Clone the repo +``` sh +git clone --recursive https://github.com/FunAudioLLM/CosyVoice.git +# If you failed to clone submodule due to network failures, please run following command until success +cd CosyVoice +git submodule update --init --recursive +``` + +- Install Conda: please see https://docs.conda.io/en/latest/miniconda.html +- Create Conda env: + +``` sh +conda create -n cosyvoice python=3.8 +conda activate cosyvoice +# pynini is required by WeTextProcessing, use conda to install it as it can be executed on all platform. +conda install -y -c conda-forge pynini==2.1.5 +pip install -r requirements.txt -i https://mirrors.aliyun.com/pypi/simple/ --trusted-host=mirrors.aliyun.com + +# If you encounter sox compatibility issues +# ubuntu +sudo apt-get install sox libsox-dev +# centos +sudo yum install sox sox-devel +``` + +**Model download** + +We strongly recommend that you download our pretrained `CosyVoice-300M` `CosyVoice-300M-SFT` `CosyVoice-300M-Instruct` model and `CosyVoice-ttsfrd` resource. + +If you are expert in this field, and you are only interested in training your own CosyVoice model from scratch, you can skip this step. + +``` python +# SDK模型下载 +from modelscope import snapshot_download +snapshot_download('iic/CosyVoice-300M', local_dir='pretrained_models/CosyVoice-300M') +snapshot_download('iic/CosyVoice-300M-25Hz', local_dir='pretrained_models/CosyVoice-300M-25Hz') +snapshot_download('iic/CosyVoice-300M-SFT', local_dir='pretrained_models/CosyVoice-300M-SFT') +snapshot_download('iic/CosyVoice-300M-Instruct', local_dir='pretrained_models/CosyVoice-300M-Instruct') +snapshot_download('iic/CosyVoice-ttsfrd', local_dir='pretrained_models/CosyVoice-ttsfrd') +``` + +``` sh +# git模型下载，请确保已安装git lfs +mkdir -p pretrained_models +git clone https://www.modelscope.cn/iic/CosyVoice-300M.git pretrained_models/CosyVoice-300M +git clone https://www.modelscope.cn/iic/CosyVoice-300M-25Hz.git pretrained_models/CosyVoice-300M-25Hz +git clone https://www.modelscope.cn/iic/CosyVoice-300M-SFT.git pretrained_models/CosyVoice-300M-SFT +git clone https://www.modelscope.cn/iic/CosyVoice-300M-Instruct.git pretrained_models/CosyVoice-300M-Instruct +git clone https://www.modelscope.cn/iic/CosyVoice-ttsfrd.git pretrained_models/CosyVoice-ttsfrd +``` + +Optionaly, you can unzip `ttsfrd` resouce and install `ttsfrd` package for better text normalization performance. + +Notice that this step is not necessary. If you do not install `ttsfrd` package, we will use WeTextProcessing by default. + +``` sh +cd pretrained_models/CosyVoice-ttsfrd/ +unzip resource.zip -d . +pip install ttsfrd-0.3.6-cp38-cp38-linux_x86_64.whl +``` + +**Basic Usage** + +For zero_shot/cross_lingual inference, please use `CosyVoice-300M` model. +For sft inference, please use `CosyVoice-300M-SFT` model. +For instruct inference, please use `CosyVoice-300M-Instruct` model. +First, add `third_party/Matcha-TTS` to your `PYTHONPATH`. + +``` sh +export PYTHONPATH=third_party/Matcha-TTS +``` + +``` python +from cosyvoice.cli.cosyvoice import CosyVoice +from cosyvoice.utils.file_utils import load_wav +import torchaudio + +cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-SFT', load_jit=True, load_onnx=False, fp16=True) +# sft usage +print(cosyvoice.list_avaliable_spks()) +# change stream=True for chunk stream inference +for i, j in enumerate(cosyvoice.inference_sft('你好，我是通义生成式语音大模型，请问有什么可以帮您的吗？', '中文女', stream=False)): + torchaudio.save('sft_{}.wav'.format(i), j['tts_speech'], 22050) + +cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-25Hz') # or change to pretrained_models/CosyVoice-300M for 50Hz inference +# zero_shot usage, <|zh|><|en|><|jp|><|yue|><|ko|> for Chinese/English/Japanese/Cantonese/Korean +prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000) +for i, j in enumerate(cosyvoice.inference_zero_shot('收到好友从远方寄来的生日礼物，那份意外的惊喜与深深的祝福让我心中充满了甜蜜的快乐，笑容如花儿般绽放。', '希望你以后能够做的比我还好呦。', prompt_speech_16k, stream=False)): + torchaudio.save('zero_shot_{}.wav'.format(i), j['tts_speech'], 22050) +# cross_lingual usage +prompt_speech_16k = load_wav('cross_lingual_prompt.wav', 16000) +for i, j in enumerate(cosyvoice.inference_cross_lingual('<|en|>And then later on, fully acquiring that company. So keeping management in line, interest in line with the asset that\'s coming into the family is a reason why sometimes we don\'t buy the whole thing.', prompt_speech_16k, stream=False)): + torchaudio.save('cross_lingual_{}.wav'.format(i), j['tts_speech'], 22050) +# vc usage +prompt_speech_16k = load_wav('zero_shot_prompt.wav', 16000) +source_speech_16k = load_wav('cross_lingual_prompt.wav', 16000) +for i, j in enumerate(cosyvoice.inference_vc(source_speech_16k, prompt_speech_16k, stream=False)): + torchaudio.save('vc_{}.wav'.format(i), j['tts_speech'], 22050) + +cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-Instruct') +# instruct usage, support [laughter][breath] +for i, j in enumerate(cosyvoice.inference_instruct('在面对挑战时，他展现了非凡的勇气与智慧。', '中文男', 'Theo \'Crimson\', is a fiery, passionate rebel leader. Fights with fervor for justice, but struggles with impulsiveness.', stream=False)): + torchaudio.save('instruct_{}.wav'.format(i), j['tts_speech'], 22050) +``` + +**Start web demo** + +You can use our web demo page to get familiar with CosyVoice quickly. +We support sft/zero_shot/cross_lingual/instruct inference in web demo. + +Please see the demo website for details. + +``` python +# change iic/CosyVoice-300M-SFT for sft inference, or iic/CosyVoice-300M-Instruct for instruct inference +python3 webui.py --port 50000 --model_dir pretrained_models/CosyVoice-300M +``` + +**Advanced Usage** + +For advanced user, we have provided train and inference scripts in `examples/libritts/cosyvoice/run.sh`. +You can get familiar with CosyVoice following this recipie. + +**Build for deployment** + +Optionally, if you want to use grpc for service deployment, +you can run following steps. Otherwise, you can just ignore this step. + +``` sh +cd runtime/python +docker build -t cosyvoice:v1.0 . +# change iic/CosyVoice-300M to iic/CosyVoice-300M-Instruct if you want to use instruct inference +# for grpc usage +docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/grpc && python3 server.py --port 50000 --max_conc 4 --model_dir iic/CosyVoice-300M && sleep infinity" +cd grpc && python3 client.py --port 50000 --mode +# for fastapi usage +docker run -d --runtime=nvidia -p 50000:50000 cosyvoice:v1.0 /bin/bash -c "cd /opt/CosyVoice/CosyVoice/runtime/python/fastapi && python3 server.py --port 50000 --model_dir iic/CosyVoice-300M && sleep infinity" +cd fastapi && python3 client.py --port 50000 --mode +``` + +## Discussion & Communication + +You can directly discuss on [Github Issues](https://github.com/FunAudioLLM/CosyVoice/issues). + +You can also scan the QR code to join our official Dingding chat group. + + + +## Acknowledge + +1. We borrowed a lot of code from [FunASR](https://github.com/modelscope/FunASR). +2. We borrowed a lot of code from [FunCodec](https://github.com/modelscope/FunCodec). +3. We borrowed a lot of code from [Matcha-TTS](https://github.com/shivammehta25/Matcha-TTS). +4. We borrowed a lot of code from [AcademiCodec](https://github.com/yangdongchao/AcademiCodec). +5. We borrowed a lot of code from [WeNet](https://github.com/wenet-e2e/wenet). + +## Disclaimer +The content provided above is for academic purposes only and is intended to demonstrate technical capabilities. Some examples are sourced from the internet. 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