diff --git a/audio_processor.py b/audio_processor.py new file mode 100644 index 0000000..c65e8fd --- /dev/null +++ b/audio_processor.py @@ -0,0 +1,67 @@ +#!/usr/bin/env python3 +""" +音频处理模块 +负责音频文件的验证和预处理 +""" + +import os +import logging +from typing import Union, Any +from faster_whisper import decode_audio + +# 日志配置 +logger = logging.getLogger(__name__) + +def validate_audio_file(audio_path: str) -> str: + """ + 验证音频文件是否有效 + + Args: + audio_path: 音频文件路径 + + Returns: + str: 验证结果，"ok"表示验证通过，否则返回错误信息 + """ + # 验证参数 + if not os.path.exists(audio_path): + return f"错误: 音频文件不存在: {audio_path}" + + # 验证文件格式 + supported_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"] + file_ext = os.path.splitext(audio_path)[1].lower() + if file_ext not in supported_formats: + return f"错误: 不支持的音频格式: {file_ext}。支持的格式: {', '.join(supported_formats)}" + + # 验证文件大小 + try: + file_size = os.path.getsize(audio_path) + if file_size == 0: + return f"错误: 音频文件为空: {audio_path}" + + # 大文件警告（超过1GB） + if file_size > 1024 * 1024 * 1024: + logger.warning(f"警告: 文件大小超过1GB，可能需要较长处理时间: {audio_path}") + except Exception as e: + logger.error(f"检查文件大小失败: {str(e)}") + return f"错误: 检查文件大小失败: {str(e)}" + + return "ok" + +def process_audio(audio_path: str) -> Union[str, Any]: + """ + 处理音频文件，进行解码和预处理 + + Args: + audio_path: 音频文件路径 + + Returns: + Union[str, Any]: 处理后的音频数据或原始文件路径 + """ + # 尝试使用decode_audio预处理音频，以处理更多格式 + try: + audio_data = decode_audio(audio_path) + logger.info(f"成功预处理音频: {os.path.basename(audio_path)}") + return audio_data + except Exception as audio_error: + logger.warning(f"音频预处理失败，将直接使用文件路径: {str(audio_error)}") + return audio_path \ No newline at end of file diff --git a/formatters.py b/formatters.py new file mode 100644 index 0000000..ff6da8a --- /dev/null +++ b/formatters.py @@ -0,0 +1,131 @@ +#!/usr/bin/env python3 +""" +格式化输出模块 +负责将转录结果格式化为不同的输出格式（VTT、SRT、JSON） +""" + +import json +from typing import List, Dict, Any + +def format_vtt(segments: List) -> str: + """ + 将转录结果格式化为VTT + + Args: + segments: 转录段落列表 + + Returns: + str: VTT格式的字幕内容 + """ + vtt_content = "WEBVTT\n\n" + + for segment in segments: + start = format_timestamp(segment.start) + end = format_timestamp(segment.end) + text = segment.text.strip() + + if text: + vtt_content += f"{start} --> {end}\n{text}\n\n" + + return vtt_content + +def format_srt(segments: List) -> str: + """ + 将转录结果格式化为SRT + + Args: + segments: 转录段落列表 + + Returns: + str: SRT格式的字幕内容 + """ + srt_content = "" + index = 1 + + for segment in segments: + start = format_timestamp_srt(segment.start) + end = format_timestamp_srt(segment.end) + text = segment.text.strip() + + if text: + srt_content += f"{index}\n{start} --> {end}\n{text}\n\n" + index += 1 + + return srt_content + +def format_json(segments: List, info: Any) -> str: + """ + 将转录结果格式化为JSON + + Args: + segments: 转录段落列表 + info: 转录信息对象 + + Returns: + str: JSON格式的转录结果 + """ + result = { + "segments": [{ + "id": i, + "start": segment.start, + "end": segment.end, + "text": segment.text.strip(), + "words": [{ + "word": word.word, + "start": word.start, + "end": word.end, + "probability": word.probability + } for word in segment.words] if hasattr(segment, 'words') and segment.words else [] + } for i, segment in enumerate(segments)], + "language": info.language, + "language_probability": info.language_probability if hasattr(info, 'language_probability') else None, + "duration": info.duration, + "all_language_probs": info.all_language_probs if hasattr(info, 'all_language_probs') else None + } + return json.dumps(result, indent=2, ensure_ascii=False) + +def format_timestamp(seconds: float) -> str: + """ + 格式化时间戳为VTT格式 + + Args: + seconds: 秒数 + + Returns: + str: 格式化的时间戳 (HH:MM:SS.mmm) + """ + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + seconds = seconds % 60 + return f"{hours:02d}:{minutes:02d}:{seconds:06.3f}" + +def format_timestamp_srt(seconds: float) -> str: + """ + 格式化时间戳为SRT格式 + + Args: + seconds: 秒数 + + Returns: + str: 格式化的时间戳 (HH:MM:SS,mmm) + """ + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = int(seconds % 60) + msecs = int((seconds - int(seconds)) * 1000) + return f"{hours:02d}:{minutes:02d}:{secs:02d},{msecs:03d}" + +def format_time(seconds: float) -> str: + """ + 格式化时间为可读格式 + + Args: + seconds: 秒数 + + Returns: + str: 格式化的时间 (HH:MM:SS) + """ + hours = int(seconds // 3600) + minutes = int((seconds % 3600) // 60) + secs = int(seconds % 60) + return f"{hours:02d}:{minutes:02d}:{secs:02d}" \ No newline at end of file diff --git a/main.py b/main.py deleted file mode 100644 index a7bde55..0000000 --- a/main.py +++ /dev/null @@ -1,6 +0,0 @@ -def main(): - print("Hello from fast-whisper-mcp-server!") - - -if __name__ == "__main__": - main() diff --git a/model_manager.py b/model_manager.py new file mode 100644 index 0000000..bb9a991 --- /dev/null +++ b/model_manager.py @@ -0,0 +1,176 @@ +#!/usr/bin/env python3 +""" +模型管理模块 +负责Whisper模型的加载、缓存和管理 +""" + +import os +import time +import logging +from typing import Dict, Any +import torch +from faster_whisper import WhisperModel, BatchedInferencePipeline + +# 日志配置 +logger = logging.getLogger(__name__) + +# 全局模型实例缓存 +model_instances = {} + +def get_whisper_model(model_name: str, device: str, compute_type: str) -> Dict[str, Any]: + """ + 获取或创建Whisper模型实例 + + Args: + model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3) + device: 运行设备 (cpu, cuda, auto) + compute_type: 计算类型 (float16, int8, auto) + + Returns: + dict: 包含模型实例和配置的字典 + """ + global model_instances + + # 验证模型名称 + valid_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2", "large-v3"] + if model_name not in valid_models: + raise ValueError(f"无效的模型名称: {model_name}。有效的模型: {', '.join(valid_models)}") + + # 自动检测设备 + if device == "auto": + device = "cuda" if torch.cuda.is_available() else "cpu" + compute_type = "float16" if device == "cuda" else "int8" + + # 验证设备和计算类型 + if device not in ["cpu", "cuda"]: + raise ValueError(f"无效的设备: {device}。有效的设备: cpu, cuda") + + if device == "cuda" and not torch.cuda.is_available(): + logger.warning("CUDA不可用，自动切换到CPU") + device = "cpu" + compute_type = "int8" + + if compute_type not in ["float16", "int8"]: + raise ValueError(f"无效的计算类型: {compute_type}。有效的计算类型: float16, int8") + + if device == "cpu" and compute_type == "float16": + logger.warning("CPU设备不支持float16计算类型，自动切换到int8") + compute_type = "int8" + + # 生成模型键 + model_key = f"{model_name}_{device}_{compute_type}" + + # 如果模型已实例化，直接返回 + if model_key in model_instances: + logger.info(f"使用缓存的模型实例: {model_key}") + return model_instances[model_key] + + # 清理GPU内存（如果使用CUDA） + if device == "cuda": + torch.cuda.empty_cache() + + # 实例化模型 + try: + logger.info(f"加载Whisper模型: {model_name} 设备: {device} 计算类型: {compute_type}") + + # 基础模型 + model = WhisperModel( + model_name, + device=device, + compute_type=compute_type, + download_root=os.environ.get("WHISPER_MODEL_DIR", None) # 支持自定义模型目录 + ) + + # 批处理设置 - 默认启用批处理以提高速度 + batched_model = None + batch_size = 0 + + if device == "cuda": # 只在CUDA设备上使用批处理 + # 根据显存大小确定合适的批大小 + if torch.cuda.is_available(): + gpu_mem = torch.cuda.get_device_properties(0).total_memory + free_mem = gpu_mem - torch.cuda.memory_allocated() + # 根据GPU显存动态调整批大小 + if free_mem > 16e9: # >16GB + batch_size = 32 + elif free_mem > 12e9: # >12GB + batch_size = 16 + elif free_mem > 8e9: # >8GB + batch_size = 8 + elif free_mem > 4e9: # >4GB + batch_size = 4 + else: # 较小显存 + batch_size = 2 + + logger.info(f"可用GPU显存: {free_mem / 1e9:.2f} GB") + else: + batch_size = 8 # 默认值 + + logger.info(f"启用批处理加速，批大小: {batch_size}") + batched_model = BatchedInferencePipeline(model=model) + + # 创建结果对象 + result = { + 'model': model, + 'device': device, + 'compute_type': compute_type, + 'batched_model': batched_model, + 'batch_size': batch_size, + 'load_time': time.time() + } + + # 缓存实例 + model_instances[model_key] = result + return result + + except Exception as e: + logger.error(f"加载模型失败: {str(e)}") + raise + +def get_model_info() -> str: + """ + 获取可用的Whisper模型信息 + + Returns: + str: 模型信息的JSON字符串 + """ + import json + + models = [ + "tiny", "base", "small", "medium", "large-v1", "large-v2", "large-v3" + ] + devices = ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"] + compute_types = ["float16", "int8"] if torch.cuda.is_available() else ["int8"] + + # 支持的语言列表 + languages = { + "zh": "中文", "en": "英语", "ja": "日语", "ko": "韩语", "de": "德语", + "fr": "法语", "es": "西班牙语", "ru": "俄语", "it": "意大利语", + "pt": "葡萄牙语", "nl": "荷兰语", "ar": "阿拉伯语", "hi": "印地语", + "tr": "土耳其语", "vi": "越南语", "th": "泰语", "id": "印尼语" + } + + # 支持的音频格式 + audio_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"] + + info = { + "available_models": models, + "default_model": "large-v3", + "available_devices": devices, + "default_device": "cuda" if torch.cuda.is_available() else "cpu", + "available_compute_types": compute_types, + "default_compute_type": "float16" if torch.cuda.is_available() else "int8", + "cuda_available": torch.cuda.is_available(), + "supported_languages": languages, + "supported_audio_formats": audio_formats, + "version": "0.1.1" + } + + if torch.cuda.is_available(): + info["gpu_info"] = { + "name": torch.cuda.get_device_name(0), + "memory_total": f"{torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB", + "memory_available": f"{torch.cuda.get_device_properties(0).total_memory / 1e9 - torch.cuda.memory_allocated() / 1e9:.2f} GB" + } + + return json.dumps(info, indent=2) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 471c697..8d0a20a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,4 +4,6 @@ version = "0.1.0" description = "Add your description here" readme = "README.md" requires-python = ">=3.12" -dependencies = [] +dependencies = [ + "faster-whisper>=1.1.1", +] diff --git a/requirements.txt b/requirements.txt index 2a7aa67..3319a2a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,22 @@ -faster-whisper>=0.9.0 +# uv pip install -r ./requirements.txt --index-url https://download.pytorch.org/whl/cu126 +faster-whisper torch==2.6.0+cu126 torchaudio==2.6.0+cu126 -mcp[cli]>=1.2.0 + +# uv pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126 +# pip install faster-whisper>=0.9.0 +# pip install mcp[cli]>=1.2.0 + +# PyTorch安装指南: +# 请根据您的CUDA版本安装适当版本的PyTorch: +# +# • CUDA 12.6: +# pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126 +# +# • CUDA 12.1: +# pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121 +# +# • CPU版本: +# pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cpu +# +# 可用命令`nvcc --version`或`nvidia-smi`查看CUDA版本 \ No newline at end of file diff --git a/transcriber.py b/transcriber.py new file mode 100644 index 0000000..fa7fc2e --- /dev/null +++ b/transcriber.py @@ -0,0 +1,326 @@ +#!/usr/bin/env python3 +""" +转录核心模块 +包含音频转录的核心逻辑 +""" + +import os +import time +import logging +from typing import Dict, Any, Tuple, List, Optional, Union + +from model_manager import get_whisper_model +from audio_processor import validate_audio_file, process_audio +from formatters import format_vtt, format_srt, format_json, format_time + +# 日志配置 +logger = logging.getLogger(__name__) + +def transcribe_audio( + audio_path: str, + model_name: str = "large-v3", + device: str = "auto", + compute_type: str = "auto", + language: str = None, + output_format: str = "vtt", + beam_size: int = 5, + temperature: float = 0.0, + initial_prompt: str = None, + output_directory: str = None +) -> str: + """ + 使用Faster Whisper转录音频文件 + + Args: + audio_path: 音频文件路径 + model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3) + device: 运行设备 (cpu, cuda, auto) + compute_type: 计算类型 (float16, int8, auto) + language: 语言代码 (如zh, en, ja等，默认自动检测) + output_format: 输出格式 (vtt, srt或json) + beam_size: 波束搜索大小，较大的值可能提高准确性但会降低速度 + temperature: 采样温度，贪婪解码 + initial_prompt: 初始提示文本，可以帮助模型更好地理解上下文 + output_directory: 输出目录路径，默认为音频文件所在目录 + + Returns: + str: 转录结果，格式为VTT字幕或JSON + """ + # 验证音频文件 + validation_result = validate_audio_file(audio_path) + if validation_result != "ok": + return validation_result + + try: + # 获取模型实例 + model_instance = get_whisper_model(model_name, device, compute_type) + + # 验证语言代码 + supported_languages = { + "zh": "中文", "en": "英语", "ja": "日语", "ko": "韩语", "de": "德语", + "fr": "法语", "es": "西班牙语", "ru": "俄语", "it": "意大利语", + "pt": "葡萄牙语", "nl": "荷兰语", "ar": "阿拉伯语", "hi": "印地语", + "tr": "土耳其语", "vi": "越南语", "th": "泰语", "id": "印尼语" + } + + if language is not None and language not in supported_languages: + logger.warning(f"未知的语言代码: {language}，将使用自动检测") + language = None + + # 设置转录参数 + options = { + "language": language, + "vad_filter": True, # 使用语音活动检测 + "vad_parameters": {"min_silence_duration_ms": 500}, # VAD参数优化 + "beam_size": beam_size, + "temperature": temperature, + "initial_prompt": initial_prompt, + "word_timestamps": True, # 启用单词级时间戳 + "suppress_tokens": [-1], # 抑制特殊标记 + "condition_on_previous_text": True, # 基于前文进行条件生成 + "compression_ratio_threshold": 2.4 # 压缩比阈值，用于过滤重复内容 + } + + start_time = time.time() + logger.info(f"开始转录文件: {os.path.basename(audio_path)}") + + # 处理音频 + audio_source = process_audio(audio_path) + + # 执行转录 - 优先使用批处理模型 + if model_instance['batched_model'] is not None and model_instance['device'] == 'cuda': + logger.info("使用批处理加速进行转录...") + # 批处理模型需要单独设置batch_size参数 + segments, info = model_instance['batched_model'].transcribe( + audio_source, + batch_size=model_instance['batch_size'], + **options + ) + else: + logger.info("使用标准模型进行转录...") + segments, info = model_instance['model'].transcribe(audio_source, **options) + + # 将生成器转换为列表 + segment_list = list(segments) + + if not segment_list: + return "转录失败，未获得结果" + + # 记录转录信息 + elapsed_time = time.time() - start_time + logger.info(f"转录完成，用时: {elapsed_time:.2f}秒，检测语言: {info.language}，音频长度: {info.duration:.2f}秒") + + # 格式化转录结果 + if output_format.lower() == "vtt": + transcription_result = format_vtt(segment_list) + elif output_format.lower() == "srt": + transcription_result = format_srt(segment_list) + else: + transcription_result = format_json(segment_list, info) + + # 获取音频文件的目录和文件名 + audio_dir = os.path.dirname(audio_path) + audio_filename = os.path.splitext(os.path.basename(audio_path))[0] + + # 设置输出目录 + if output_directory is None: + output_dir = audio_dir + else: + output_dir = output_directory + # 确保输出目录存在 + os.makedirs(output_dir, exist_ok=True) + + # 生成带有时间戳的文件名 + timestamp = time.strftime("%Y%m%d%H%M%S") + output_filename = f"{audio_filename}_{timestamp}.{output_format.lower()}" + output_path = os.path.join(output_dir, output_filename) + + # 将转录结果写入文件 + try: + with open(output_path, "w", encoding="utf-8") as f: + f.write(transcription_result) + logger.info(f"转录结果已保存到: {output_path}") + return f"转录成功，结果已保存到: {output_path}" + except Exception as e: + logger.error(f"保存转录结果失败: {str(e)}") + return f"转录成功，但保存结果失败: {str(e)}" + + except Exception as e: + logger.error(f"转录失败: {str(e)}") + return f"转录过程中发生错误: {str(e)}" + + +def report_progress(current: int, total: int, elapsed_time: float) -> str: + """ + 生成进度报告 + + Args: + current: 当前处理的项目数 + total: 总项目数 + elapsed_time: 已用时间（秒） + + Returns: + str: 格式化的进度报告 + """ + progress = current / total * 100 + eta = (elapsed_time / current) * (total - current) if current > 0 else 0 + return (f"进度: {current}/{total} ({progress:.1f}%)" + + f" | 已用时间: {format_time(elapsed_time)}" + + f" | 预计剩余: {format_time(eta)}") + +def batch_transcribe( + audio_folder: str, + output_folder: str = None, + model_name: str = "large-v3", + device: str = "auto", + compute_type: str = "auto", + language: str = None, + output_format: str = "vtt", + beam_size: int = 5, + temperature: float = 0.0, + initial_prompt: str = None, + parallel_files: int = 1 +) -> str: + """ + 批量转录文件夹中的音频文件 + + Args: + audio_folder: 包含音频文件的文件夹路径 + output_folder: 输出文件夹路径，默认为audio_folder下的transcript子文件夹 + model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3) + device: 运行设备 (cpu, cuda, auto) + compute_type: 计算类型 (float16, int8, auto) + language: 语言代码 (如zh, en, ja等，默认自动检测) + output_format: 输出格式 (vtt, srt或json) + beam_size: 波束搜索大小，较大的值可能提高准确性但会降低速度 + temperature: 采样温度，0表示贪婪解码 + initial_prompt: 初始提示文本，可以帮助模型更好地理解上下文 + parallel_files: 并行处理的文件数量（仅在CPU模式下有效） + + Returns: + str: 批处理结果摘要，包含处理时间和成功率 + """ + if not os.path.isdir(audio_folder): + return f"错误: 文件夹不存在: {audio_folder}" + + # 设置输出文件夹 + if output_folder is None: + output_folder = os.path.join(audio_folder, "transcript") + + # 确保输出目录存在 + os.makedirs(output_folder, exist_ok=True) + + # 验证输出格式 + valid_formats = ["vtt", "srt", "json"] + if output_format.lower() not in valid_formats: + return f"错误: 不支持的输出格式: {output_format}。支持的格式: {', '.join(valid_formats)}" + + # 获取所有音频文件 + audio_files = [] + supported_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"] + + for filename in os.listdir(audio_folder): + file_ext = os.path.splitext(filename)[1].lower() + if file_ext in supported_formats: + audio_files.append(os.path.join(audio_folder, filename)) + + if not audio_files: + return f"在 {audio_folder} 中未找到支持的音频文件。支持的格式: {', '.join(supported_formats)}" + + # 记录开始时间 + start_time = time.time() + total_files = len(audio_files) + logger.info(f"开始批量转录 {total_files} 个文件，输出格式: {output_format}") + + # 预加载模型以避免重复加载 + try: + get_whisper_model(model_name, device, compute_type) + logger.info(f"已预加载模型: {model_name}") + except Exception as e: + logger.error(f"预加载模型失败: {str(e)}") + return f"批处理失败: 无法加载模型 {model_name}: {str(e)}" + + # 处理每个文件 + results = [] + success_count = 0 + error_count = 0 + total_audio_duration = 0 + + # 处理每个文件 + for i, audio_path in enumerate(audio_files): + file_name = os.path.basename(audio_path) + elapsed = time.time() - start_time + + # 报告进度 + progress_msg = report_progress(i, total_files, elapsed) + logger.info(f"{progress_msg} | 当前处理: {file_name}") + + # 执行转录 + try: + result = transcribe_audio( + audio_path=audio_path, + model_name=model_name, + device=device, + compute_type=compute_type, + language=language, + output_format=output_format, + beam_size=beam_size, + temperature=temperature, + initial_prompt=initial_prompt, + output_directory=output_folder + ) + + # 检查结果是否包含错误信息 + if result.startswith("错误:") or result.startswith("转录过程中发生错误:"): + logger.error(f"转录失败: {file_name} - {result}") + results.append(f"❌ 失败: {file_name} - {result}") + error_count += 1 + continue + + # 如果转录成功，提取输出路径信息 + if result.startswith("转录成功"): + # 从返回消息中提取输出路径 + output_path = result.split(": ")[1] if ": " in result else "未知路径" + success_count += 1 + results.append(f"✅ 成功: {file_name} -> {os.path.basename(output_path)}") + + # 提取音频时长 + audio_duration = 0 + if output_format.lower() == "json": + # 尝试从输出文件中解析音频时长 + try: + import json + # 从输出文件中读取JSON内容 + with open(output_path, "r", encoding="utf-8") as json_file: + json_content = json_file.read() + json_data = json.loads(json_content) + audio_duration = json_data.get("duration", 0) + except Exception as e: + logger.warning(f"无法从JSON文件中提取音频时长: {str(e)}") + audio_duration = 0 + else: + # 尝试从文件名中提取音频信息 + try: + # 这里我们不能直接访问info对象，因为它在transcribe_audio函数的作用域内 + # 使用一个保守的估计值或从结果字符串中提取信息 + audio_duration = 0 # 默认为0 + except Exception as e: + logger.warning(f"无法从文件名中提取音频时长: {str(e)}") + audio_duration = 0 + + # 累加音频时长 + total_audio_duration += audio_duration + except Exception as e: + logger.error(f"转录过程中发生错误: {file_name} - {str(e)}") + results.append(f"❌ 失败: {file_name} - {str(e)}") + error_count += 1 + # 计算总转录时间 + total_transcription_time = time.time() - start_time + # 生成批处理结果摘要 + summary = f"批处理完成，总转录时间: {format_time(total_transcription_time)}" + summary += f" | 成功: {success_count}/{total_files}" + summary += f" | 失败: {error_count}/{total_files}" + # 输出结果 + for result in results: + 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get_model_info() -> str: - """获取可用的Whisper模型信息""" - models = [ - "tiny", "base", "small", "medium", "large-v1", "large-v2", "large-v3" - ] - devices = ["cpu", "cuda"] if torch.cuda.is_available() else ["cpu"] - compute_types = ["float16", "int8"] if torch.cuda.is_available() else ["int8"] - - # 支持的语言列表 - languages = { - "zh": "中文", "en": "英语", "ja": "日语", "ko": "韩语", "de": "德语", - "fr": "法语", "es": "西班牙语", "ru": "俄语", "it": "意大利语", - "pt": "葡萄牙语", "nl": "荷兰语", "ar": "阿拉伯语", "hi": "印地语", - "tr": "土耳其语", "vi": "越南语", "th": "泰语", "id": "印尼语" - } - - # 支持的音频格式 - audio_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"] - - info = { - "available_models": models, - "default_model": "large-v3", - "available_devices": devices, - "default_device": "cuda" if torch.cuda.is_available() else "cpu", - "available_compute_types": compute_types, - "default_compute_type": "float16" if torch.cuda.is_available() else "int8", - "cuda_available": torch.cuda.is_available(), - "supported_languages": languages, - "supported_audio_formats": audio_formats, - "version": "0.1.1" - } - - if torch.cuda.is_available(): - info["gpu_info"] = { - "name": torch.cuda.get_device_name(0), - "memory_total": f"{torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB", - "memory_available": f"{torch.cuda.get_device_properties(0).total_memory / 1e9 - torch.cuda.memory_allocated() / 1e9:.2f} GB" - } - - return json.dumps(info, indent=2) - -def get_whisper_model(model_name: str, device: str, compute_type: str) -> Dict: +def get_model_info_api() -> str: """ - 获取或创建Whisper模型实例 - - Args: - model_name: 模型名称 (tiny, base, small, medium, large-v1, large-v2, large-v3) - device: 运行设备 (cpu, cuda, auto) - compute_type: 计算类型 (float16, int8, auto) - - Returns: - dict: 包含模型实例和配置的字典 + 获取可用的Whisper模型信息 """ - global model_instances - - # 验证模型名称 - valid_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2", "large-v3"] - if model_name not in valid_models: - raise ValueError(f"无效的模型名称: {model_name}。有效的模型: {', '.join(valid_models)}") - - # 自动检测设备 - if device == "auto": - device = "cuda" if torch.cuda.is_available() else "cpu" - compute_type = "float16" if device == "cuda" else "int8" - - # 验证设备和计算类型 - if device not in ["cpu", "cuda"]: - raise ValueError(f"无效的设备: {device}。有效的设备: cpu, cuda") - - if device == "cuda" and not torch.cuda.is_available(): - logger.warning("CUDA不可用，自动切换到CPU") - device = "cpu" - compute_type = "int8" - - if compute_type not in ["float16", "int8"]: - raise ValueError(f"无效的计算类型: {compute_type}。有效的计算类型: float16, int8") - - if device == "cpu" and compute_type == "float16": - logger.warning("CPU设备不支持float16计算类型，自动切换到int8") - compute_type = "int8" - - # 生成模型键 - model_key = f"{model_name}_{device}_{compute_type}" - - # 如果模型已实例化，直接返回 - if model_key in model_instances: - logger.info(f"使用缓存的模型实例: {model_key}") - return model_instances[model_key] - - # 清理GPU内存（如果使用CUDA） - if device == "cuda": - torch.cuda.empty_cache() - - # 实例化模型 - try: - logger.info(f"加载Whisper模型: {model_name} 设备: {device} 计算类型: {compute_type}") - - # 基础模型 - model = WhisperModel( - model_name, - device=device, - compute_type=compute_type, - download_root=os.environ.get("WHISPER_MODEL_DIR", None) # 支持自定义模型目录 - ) - - # 批处理设置 - 默认启用批处理以提高速度 - batched_model = None - batch_size = 0 - - if device == "cuda": # 只在CUDA设备上使用批处理 - # 根据显存大小确定合适的批大小 - if torch.cuda.is_available(): - gpu_mem = torch.cuda.get_device_properties(0).total_memory - free_mem = gpu_mem - torch.cuda.memory_allocated() - # 根据GPU显存动态调整批大小 - if free_mem > 16e9: # >16GB - batch_size = 32 - elif free_mem > 12e9: # >12GB - batch_size = 16 - elif free_mem > 8e9: # >8GB - batch_size = 8 - elif free_mem > 4e9: # >4GB - batch_size = 4 - else: # 较小显存 - batch_size = 2 - - logger.info(f"可用GPU显存: {free_mem / 1e9:.2f} GB") - else: - batch_size = 8 # 默认值 - - logger.info(f"启用批处理加速，批大小: {batch_size}") - batched_model = BatchedInferencePipeline(model=model) - - # 创建结果对象 - result = { - 'model': model, - 'device': device, - 'compute_type': compute_type, - 'batched_model': batched_model, - 'batch_size': batch_size, - 'load_time': time.time() - } - - # 缓存实例 - model_instances[model_key] = result - return result - - except Exception as e: - logger.error(f"加载模型失败: {str(e)}") - raise - + return get_model_info() @mcp.tool() def transcribe(audio_path: str, model_name: str = "large-v3", device: str = "auto", @@ -203,200 +52,21 @@ def transcribe(audio_path: str, model_name: str = "large-v3", device: str = "aut Returns: str: 转录结果，格式为VTT字幕或JSON """ - # 验证参数 - if not os.path.exists(audio_path): - return f"错误: 音频文件不存在: {audio_path}" - - # 验证文件格式 - supported_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"] - file_ext = os.path.splitext(audio_path)[1].lower() - if file_ext not in supported_formats: - return f"错误: 不支持的音频格式: {file_ext}。支持的格式: {', '.join(supported_formats)}" - - # 验证文件大小 - try: - file_size = os.path.getsize(audio_path) - if file_size == 0: - return f"错误: 音频文件为空: {audio_path}" - - # 大文件警告（超过1GB） - if file_size > 1024 * 1024 * 1024: - logger.warning(f"警告: 文件大小超过1GB，可能需要较长处理时间: {audio_path}") - except Exception as e: - logger.error(f"检查文件大小失败: {str(e)}") - - try: - # 获取模型实例 - model_instance = get_whisper_model(model_name, device, compute_type) - - # 验证语言代码 - if language is not None: - # 支持的语言列表 - supported_languages = { - "zh": "中文", "en": "英语", "ja": "日语", "ko": "韩语", "de": "德语", - "fr": "法语", "es": "西班牙语", "ru": "俄语", "it": "意大利语", - "pt": "葡萄牙语", "nl": "荷兰语", "ar": "阿拉伯语", "hi": "印地语", - "tr": "土耳其语", "vi": "越南语", "th": "泰语", "id": "印尼语" - } - - if language not in supported_languages: - logger.warning(f"未知的语言代码: {language}，将使用自动检测") - language = None - - # 设置转录参数 - options = { - "language": language, - "vad_filter": True, # 使用语音活动检测 - "vad_parameters": {"min_silence_duration_ms": 500}, # VAD参数优化 - "beam_size": beam_size, - "temperature": temperature, - "initial_prompt": initial_prompt, - "word_timestamps": True, # 启用单词级时间戳 - "suppress_tokens": [-1], # 抑制特殊标记 - "condition_on_previous_text": True, # 基于前文进行条件生成 - "compression_ratio_threshold": 2.4 # 压缩比阈值，用于过滤重复内容 - } - - start_time = time.time() - logger.info(f"开始转录文件: {os.path.basename(audio_path)}") - - # 尝试使用decode_audio预处理音频，以处理更多格式 - try: - audio_data = decode_audio(audio_path) - audio_source = audio_data - logger.info(f"成功预处理音频: {os.path.basename(audio_path)}") - except Exception as audio_error: - logger.warning(f"音频预处理失败，将直接使用文件路径: {str(audio_error)}") - audio_source = audio_path - - # 执行转录 - 优先使用批处理模型 - if model_instance['batched_model'] is not None and model_instance['device'] == 'cuda': - logger.info("使用批处理加速进行转录...") - # 批处理模型需要单独设置batch_size参数 - segments, info = model_instance['batched_model'].transcribe( - audio_source, - batch_size=model_instance['batch_size'], - **options - ) - else: - logger.info("使用标准模型进行转录...") - segments, info = model_instance['model'].transcribe(audio_source, **options) - - # 将生成器转换为列表 - segment_list = list(segments) - - if not segment_list: - return "转录失败，未获得结果" - - # 记录转录信息 - elapsed_time = time.time() - start_time - logger.info(f"转录完成，用时: {elapsed_time:.2f}秒，检测语言: {info.language}，音频长度: {info.duration:.2f}秒") - - # 格式化转录结果 - if output_format.lower() == "vtt": - transcription_result = format_vtt(segment_list) - elif output_format.lower() == "srt": - transcription_result = format_srt(segment_list) - else: - transcription_result = format_json(segment_list, info) - - # 获取音频文件的目录和文件名 - audio_dir = os.path.dirname(audio_path) - audio_filename = os.path.splitext(os.path.basename(audio_path))[0] - - # 设置输出目录 - if output_directory is None: - output_dir = audio_dir - else: - output_dir = output_directory - # 确保输出目录存在 - os.makedirs(output_dir, exist_ok=True) - - # 生成带有时间戳的文件名 - timestamp = time.strftime("%Y%m%d%H%M%S") - output_filename = f"{audio_filename}_{timestamp}.{output_format.lower()}" - output_path = os.path.join(output_dir, output_filename) - - # 将转录结果写入文件 - try: - with open(output_path, "w", encoding="utf-8") as f: - f.write(transcription_result) - logger.info(f"转录结果已保存到: {output_path}") - return f"转录成功，结果已保存到: {output_path}" - except Exception as e: - logger.error(f"保存转录结果失败: {str(e)}") - return f"转录成功，但保存结果失败: {str(e)}" - - except Exception as e: - logger.error(f"转录失败: {str(e)}") - return f"转录过程中发生错误: {str(e)}" -def format_vtt(segments) -> str: - """将转录结果格式化为VTT""" - vtt_content = "WEBVTT\n\n" - - for segment in segments: - start = format_timestamp(segment.start) - end = format_timestamp(segment.end) - text = segment.text.strip() - - if text: - vtt_content += f"{start} --> {end}\n{text}\n\n" - - return vtt_content - -def format_srt(segments) -> str: - """将转录结果格式化为SRT""" - srt_content = "" - - for segment in segments: - start = format_timestamp_srt(segment.start) - end = format_timestamp_srt(segment.end) - text = segment.text.strip() - - if text: - srt_content += f"{len(srt_content.splitlines()) + 1}\n{start} --> {end}\n{text}\n\n" - - return srt_content - -def format_json(segments, info) -> str: - """将转录结果格式化为JSON""" - result = { - "segments": [{ - "id": segments.index(segment), - "start": segment.start, - "end": segment.end, - "text": segment.text.strip(), - "words": [{ - "word": word.word, - "start": word.start, - "end": word.end, - "probability": word.probability - } for word in segment.words] if hasattr(segment, 'words') and segment.words else [] - } for segment in segments], - "language": info.language, - "language_probability": info.language_probability if hasattr(info, 'language_probability') else None, - "duration": info.duration, - "all_language_probs": info.all_language_probs if hasattr(info, 'all_language_probs') else None - } - return json.dumps(result, indent=2, ensure_ascii=False) - -def format_timestamp(seconds: float) -> str: - """格式化时间戳为VTT格式""" - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - seconds = seconds % 60 - return f"{hours:02d}:{minutes:02d}:{seconds:06.3f}" - -def format_timestamp_srt(seconds: float) -> str: - """格式化时间戳为SRT格式""" - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - secs = int(seconds % 60) - msecs = int((seconds - int(seconds)) * 1000) - return f"{hours:02d}:{minutes:02d}:{secs:02d},{msecs:03d}" + return transcribe_audio( + audio_path=audio_path, + model_name=model_name, + device=device, + compute_type=compute_type, + language=language, + output_format=output_format, + beam_size=beam_size, + temperature=temperature, + initial_prompt=initial_prompt, + output_directory=output_directory + ) @mcp.tool() -def batch_transcribe(audio_folder: str, output_folder: str = None, model_name: str = "large-v3", +def batch_transcribe_audio(audio_folder: str, output_folder: str = None, model_name: str = "large-v3", device: str = "auto", compute_type: str = "auto", language: str = None, output_format: str = "vtt", beam_size: int = 5, temperature: float = 0.0, initial_prompt: str = None, parallel_files: int = 1) -> str: @@ -419,163 +89,20 @@ def batch_transcribe(audio_folder: str, output_folder: str = None, model_name: s Returns: str: 批处理结果摘要，包含处理时间和成功率 """ - if not os.path.isdir(audio_folder): - return f"错误: 文件夹不存在: {audio_folder}" - - # 设置输出文件夹 - if output_folder is None: - output_folder = os.path.join(audio_folder, "transcript") - - # 确保输出目录存在 - os.makedirs(output_folder, exist_ok=True) - - # 验证输出格式 - valid_formats = ["vtt", "srt", "json"] - if output_format.lower() not in valid_formats: - return f"错误: 不支持的输出格式: {output_format}。支持的格式: {', '.join(valid_formats)}" - - # 获取所有音频文件 - audio_files = [] - supported_formats = [".mp3", ".wav", ".m4a", ".flac", ".ogg", ".aac"] - - for filename in os.listdir(audio_folder): - file_ext = os.path.splitext(filename)[1].lower() - if file_ext in supported_formats: - audio_files.append(os.path.join(audio_folder, filename)) - - if not audio_files: - return f"在 {audio_folder} 中未找到支持的音频文件。支持的格式: {', '.join(supported_formats)}" - - # 记录开始时间 - start_time = time.time() - total_files = len(audio_files) - logger.info(f"开始批量转录 {total_files} 个文件，输出格式: {output_format}") - - # 处理每个文件 - results = [] - success_count = 0 - error_count = 0 - total_audio_duration = 0 - - # 预加载模型以避免重复加载 - try: - get_whisper_model(model_name, device, compute_type) - logger.info(f"已预加载模型: {model_name}") - except Exception as e: - logger.error(f"预加载模型失败: {str(e)}") - return f"批处理失败: 无法加载模型 {model_name}: {str(e)}" - - # 处理进度报告函数 - def report_progress(current, total, elapsed_time): - progress = current / total * 100 - eta = (elapsed_time / current) * (total - current) if current > 0 else 0 - return (f"进度: {current}/{total} ({progress:.1f}%)" + - f" | 已用时间: {format_time(elapsed_time)}" + - f" | 预计剩余: {format_time(eta)}") - - # 格式化时间函数 - def format_time(seconds): - hours = int(seconds // 3600) - minutes = int((seconds % 3600) // 60) - secs = int(seconds % 60) - return f"{hours:02d}:{minutes:02d}:{secs:02d}" - - # 处理每个文件 - for i, audio_path in enumerate(audio_files): - file_name = os.path.basename(audio_path) - elapsed = time.time() - start_time - - # 报告进度 - progress_msg = report_progress(i, total_files, elapsed) - logger.info(f"{progress_msg} | 当前处理: {file_name}") - - # 设置输出文件路径 - base_name = os.path.splitext(file_name)[0] - output_ext = "." + output_format.lower() - if output_format.lower() == "json": - output_ext = ".json" - elif output_format.lower() == "vtt": - output_ext = ".vtt" - elif output_format.lower() == "srt": - output_ext = ".srt" - - output_path = os.path.join(output_folder, f"{base_name}{output_ext}") - - # 执行转录 - try: - result = transcribe( - audio_path=audio_path, - model_name=model_name, - device=device, - compute_type=compute_type, - language=language, - output_format=output_format, - beam_size=beam_size, - temperature=temperature, - initial_prompt=initial_prompt, - output_directory=output_folder - ) - - # 检查结果是否包含错误信息 - if result.startswith("错误:") or result.startswith("转录过程中发生错误:"): - logger.error(f"转录失败: {file_name} - {result}") - results.append(f"❌ 失败: {file_name} - {result}") - error_count += 1 - continue - - # 如果转录成功，提取输出路径信息 - if result.startswith("转录成功"): - # 从返回消息中提取输出路径 - output_path = result.split(": ")[1] if ": " in result else "未知路径" - success_count += 1 - results.append(f"✅ 成功: {file_name} -> {os.path.basename(output_path)}") - continue - - # 检查转录结果是否已成功保存 - if result.startswith("转录成功"): - logger.info(f"转录结果已保存: {file_name}") - else: - logger.error(f"转录未成功保存: {file_name} - {result}") - continue - - # 提取音频时长（如果是JSON格式） - audio_duration = 0 - if output_format.lower() == "json": - try: - json_result = json.loads(result) - audio_duration = json_result.get("duration", 0) - total_audio_duration += audio_duration - except: - pass - - success_count += 1 - duration_info = f" (时长: {audio_duration:.1f}秒)" if audio_duration > 0 else "" - results.append(f"✅ 成功: {file_name} -> {os.path.basename(output_path)}{duration_info}") - - except Exception as e: - logger.error(f"转录失败: {file_name} - {str(e)}") - results.append(f"❌ 失败: {file_name} - {str(e)}") - error_count += 1 - - # 计算总时间和处理速度 - total_time = time.time() - start_time - processing_speed = total_audio_duration / total_time if total_audio_duration > 0 and total_time > 0 else 0 - - # 生成摘要 - summary = f"批处理完成，用时: {format_time(total_time)}\n" - summary += f"成功: {success_count}/{total_files} ({success_count/total_files*100:.1f}%)\n" - if error_count > 0: - summary += f"失败: {error_count}/{total_files} ({error_count/total_files*100:.1f}%)\n" - if total_audio_duration > 0: - summary += f"总音频时长: {total_audio_duration:.1f}秒\n" - summary += f"处理速度: {processing_speed:.2f}x 实时速度\n" - summary += f"输出目录: {output_folder}\n\n" - - # 添加详细结果 - summary += "详细结果:\n" + "\n".join(results) - - return summary + return batch_transcribe( + audio_folder=audio_folder, + output_folder=output_folder, + model_name=model_name, + device=device, + compute_type=compute_type, + language=language, + output_format=output_format, + beam_size=beam_size, + temperature=temperature, + initial_prompt=initial_prompt, + parallel_files=parallel_files + ) if __name__ == "__main__": # 运行服务器 - mcp.run() + mcp.run() \ No newline at end of file