| import argparse |
| import os |
| import traceback |
|
|
| os.environ["HF_ENDPOINT"] = "https://hf-mirror.com" |
| os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE" |
|
|
| import torch |
| from faster_whisper import WhisperModel |
| from tqdm import tqdm |
|
|
| from tools.asr.config import check_fw_local_models |
|
|
| language_code_list = [ |
| "af", "am", "ar", "as", "az", |
| "ba", "be", "bg", "bn", "bo", |
| "br", "bs", "ca", "cs", "cy", |
| "da", "de", "el", "en", "es", |
| "et", "eu", "fa", "fi", "fo", |
| "fr", "gl", "gu", "ha", "haw", |
| "he", "hi", "hr", "ht", "hu", |
| "hy", "id", "is", "it", "ja", |
| "jw", "ka", "kk", "km", "kn", |
| "ko", "la", "lb", "ln", "lo", |
| "lt", "lv", "mg", "mi", "mk", |
| "ml", "mn", "mr", "ms", "mt", |
| "my", "ne", "nl", "nn", "no", |
| "oc", "pa", "pl", "ps", "pt", |
| "ro", "ru", "sa", "sd", "si", |
| "sk", "sl", "sn", "so", "sq", |
| "sr", "su", "sv", "sw", "ta", |
| "te", "tg", "th", "tk", "tl", |
| "tr", "tt", "uk", "ur", "uz", |
| "vi", "yi", "yo", "zh", "yue", |
| "auto"] |
|
|
| def execute_asr(input_folder, output_folder, model_size, language, precision): |
| if '-local' in model_size: |
| model_size = model_size[:-6] |
| model_path = f'tools/asr/models/faster-whisper-{model_size}' |
| else: |
| model_path = model_size |
| if language == 'auto': |
| language = None |
| print("loading faster whisper model:",model_size,model_path) |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' |
| try: |
| model = WhisperModel(model_path, device=device, compute_type=precision) |
| except: |
| return print(traceback.format_exc()) |
| |
| input_file_names = os.listdir(input_folder) |
| input_file_names.sort() |
|
|
| output = [] |
| output_file_name = os.path.basename(input_folder) |
| |
| for file_name in tqdm(input_file_names): |
| try: |
| file_path = os.path.join(input_folder, file_name) |
| segments, info = model.transcribe( |
| audio = file_path, |
| beam_size = 5, |
| vad_filter = True, |
| vad_parameters = dict(min_silence_duration_ms=700), |
| language = language) |
| text = '' |
|
|
| if info.language == "zh": |
| print("检测为中文文本, 转 FunASR 处理") |
| if("only_asr"not in globals()): |
| from tools.asr.funasr_asr import \ |
| only_asr |
| text = only_asr(file_path) |
|
|
| if text == '': |
| for segment in segments: |
| text += segment.text |
| output.append(f"{file_path}|{output_file_name}|{info.language.upper()}|{text}") |
| except: |
| print(traceback.format_exc()) |
| |
| output_folder = output_folder or "output/asr_opt" |
| os.makedirs(output_folder, exist_ok=True) |
| output_file_path = os.path.abspath(f'{output_folder}/{output_file_name}.list') |
|
|
| with open(output_file_path, "w", encoding="utf-8") as f: |
| f.write("\n".join(output)) |
| print(f"ASR 任务完成->标注文件路径: {output_file_path}\n") |
| return output_file_path |
|
|
| if __name__ == '__main__': |
| parser = argparse.ArgumentParser() |
| parser.add_argument("-i", "--input_folder", type=str, required=True, |
| help="Path to the folder containing WAV files.") |
| parser.add_argument("-o", "--output_folder", type=str, required=True, |
| help="Output folder to store transcriptions.") |
| parser.add_argument("-s", "--model_size", type=str, default='large-v3', |
| choices=check_fw_local_models(), |
| help="Model Size of Faster Whisper") |
| parser.add_argument("-l", "--language", type=str, default='ja', |
| choices=language_code_list, |
| help="Language of the audio files.") |
| parser.add_argument("-p", "--precision", type=str, default='float16', choices=['float16','float32','int8'], |
| help="fp16, int8 or fp32") |
|
|
| cmd = parser.parse_args() |
| output_file_path = execute_asr( |
| input_folder = cmd.input_folder, |
| output_folder = cmd.output_folder, |
| model_size = cmd.model_size, |
| language = cmd.language, |
| precision = cmd.precision, |
| ) |
|
|
