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# -*- coding:utf-8 -*-
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import argparse
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import os
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import traceback
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# from funasr.utils import version_checker
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# version_checker.check_for_update = lambda: None
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from funasr import AutoModel
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from tqdm import tqdm
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funasr_models = {} # 存储模型避免重复加载
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def only_asr(input_file, language):
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try:
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model = create_model(language)
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text = model.generate(input=input_file)[0]["text"]
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except:
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text = ""
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print(traceback.format_exc())
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return text
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def create_model(language="zh"):
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path_vad = "tools/asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch"
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path_punc = "tools/asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
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path_vad = path_vad if os.path.exists(path_vad) else "iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
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path_punc = path_punc if os.path.exists(path_punc) else "iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
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vad_model_revision = punc_model_revision = "v2.0.4"
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if language == "zh":
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path_asr = "tools/asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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path_asr = (
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path_asr
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if os.path.exists(path_asr)
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else "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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)
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model_revision = "v2.0.4"
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elif language == "yue":
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path_asr = "tools/asr/models/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online"
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path_asr = (
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path_asr
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if os.path.exists(path_asr)
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else "iic/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online"
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)
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model_revision = "master"
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path_vad = path_punc = None
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vad_model_revision = punc_model_revision = None
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###友情提示：粤语带VAD识别可能会有少量shape不对报错的，但是不带VAD可以.不带vad只能分阶段单独加标点。不过标点模型对粤语效果真的不行…
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else:
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raise ValueError("FunASR 不支持该语言" + ": " + language)
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if language in funasr_models:
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return funasr_models[language]
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else:
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model = AutoModel(
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model=path_asr,
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model_revision=model_revision,
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vad_model=path_vad,
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vad_model_revision=vad_model_revision,
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punc_model=path_punc,
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punc_model_revision=punc_model_revision,
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)
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print(f"FunASR 模型加载完成: {language.upper()}")
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funasr_models[language] = model
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return model
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def execute_asr(input_folder, output_folder, model_size, language):
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input_file_names = os.listdir(input_folder)
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input_file_names.sort()
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output = []
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output_file_name = os.path.basename(input_folder)
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model = create_model(language)
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for file_name in tqdm(input_file_names):
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try:
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print("\n" + file_name)
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file_path = os.path.join(input_folder, file_name)
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text = model.generate(input=file_path)[0]["text"]
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output.append(f"{file_path}|{output_file_name}|{language.upper()}|{text}")
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except:
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print(traceback.format_exc())
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output_folder = output_folder or "output/asr_opt"
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os.makedirs(output_folder, exist_ok=True)
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output_file_path = os.path.abspath(f"{output_folder}/{output_file_name}.list")
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with open(output_file_path, "w", encoding="utf-8") as f:
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f.write("\n".join(output))
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print(f"ASR 任务完成->标注文件路径: {output_file_path}\n")
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return output_file_path
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-i", "--input_folder", type=str, required=True, help="Path to the folder containing WAV files."
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)
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parser.add_argument("-o", "--output_folder", type=str, required=True, help="Output folder to store transcriptions.")
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parser.add_argument("-s", "--model_size", type=str, default="large", help="Model Size of FunASR is Large")
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parser.add_argument(
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"-l", "--language", type=str, default="zh", choices=["zh", "yue", "auto"], help="Language of the audio files."
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)
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parser.add_argument(
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"-p", "--precision", type=str, default="float16", choices=["float16", "float32"], help="fp16 or fp32"
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) # 还没接入
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cmd = parser.parse_args()
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execute_asr(
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input_folder=cmd.input_folder,
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output_folder=cmd.output_folder,
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model_size=cmd.model_size,
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language=cmd.language,
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)
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