mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
python cli
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游雁
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@ -76,6 +76,15 @@ Quick start for new users（[tutorial](https://alibaba-damo-academy.github.io/Fu
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FunASR supports inference and fine-tuning of models trained on industrial data for tens of thousands of hours. For more details, please refer to [modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html). It also supports training and fine-tuning of models on academic standard datasets. For more information, please refer to [egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html).
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Below is a quick start tutorial. Test audio files ([Mandarin](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav), [English]()).
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### Command-line usage
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```shell
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funasr --model paraformer-zh asr_example_zh.wav
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```
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Notes: Support recognition of single audio file, as well as file list in Kaldi-style wav.scp format: `wav_id wav_pat`
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### Speech Recognition (Non-streaming)
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```python
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from funasr import infer
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@ -70,6 +70,15 @@ FunASR开源了大量在工业数据上预训练模型，您可以在[模型许
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FunASR支持数万小时工业数据训练的模型的推理和微调，详细信息可以参阅（[modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)）；也支持学术标准数据集模型的训练和微调，详细信息可以参阅（[egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)）。
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下面为快速上手教程，测试音频（[中文](https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav)，[英文]()）
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### 可执行命令行
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```shell
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funasr --model paraformer-zh asr_example_zh.wav
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```
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注：支持单条音频文件识别，也支持文件列表，列表为kaldi风格wav.scp：`wav_id wav_path`
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### 非实时语音识别
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```python
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from funasr import infer
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@ -1,135 +1,10 @@
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"""Initialize funasr package."""
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import os
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from pathlib import Path
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import torch
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import numpy as np
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dirname = os.path.dirname(__file__)
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version_file = os.path.join(dirname, "version.txt")
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with open(version_file, "r") as f:
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__version__ = f.read().strip()
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def prepare_model(
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model: str = None,
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# mode: str = None,
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vad_model: str = None,
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punc_model: str = None,
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model_hub: str = "ms",
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cache_dir: str = None,
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**kwargs,
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):
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if not Path(model).exists():
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if model_hub == "ms" or model_hub == "modelscope":
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try:
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from modelscope.hub.snapshot_download import snapshot_download as download_tool
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model = name_maps_ms[model] if model is not None else None
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vad_model = name_maps_ms[vad_model] if vad_model is not None else None
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punc_model = name_maps_ms[punc_model] if punc_model is not None else None
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except:
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raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" \
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"\npip3 install -U modelscope\n" \
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"For the users in China, you could install with the command:\n" \
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"\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple"
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elif model_hub == "hf" or model_hub == "huggingface":
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download_tool = 0
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else:
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raise "model_hub must be on of ms or hf, but get {}".format(model_hub)
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try:
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model = download_tool(model, cache_dir=cache_dir, revision=kwargs.get("revision", None))
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print("model have been downloaded to: {}".format(model))
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except:
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raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
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model)
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if vad_model is not None and not Path(vad_model).exists():
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vad_model = download_tool(vad_model, cache_dir=cache_dir)
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print("model have been downloaded to: {}".format(vad_model))
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if punc_model is not None and not Path(punc_model).exists():
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punc_model = download_tool(punc_model, cache_dir=cache_dir)
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print("model have been downloaded to: {}".format(punc_model))
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# asr
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kwargs.update({"cmvn_file": None if model is None else os.path.join(model, "am.mvn"),
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"asr_model_file": None if model is None else os.path.join(model, "model.pb"),
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"asr_train_config": None if model is None else os.path.join(model, "config.yaml"),
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})
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mode = kwargs.get("mode", None)
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if mode is None:
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import json
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json_file = os.path.join(model, 'configuration.json')
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with open(json_file, 'r') as f:
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config_data = json.load(f)
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if config_data['task'] == "punctuation":
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mode = config_data['model']['punc_model_config']['mode']
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else:
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mode = config_data['model']['model_config']['mode']
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if vad_model is not None and "vad" not in mode:
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mode = "paraformer_vad"
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kwargs["mode"] = mode
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# vad
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kwargs.update({"vad_cmvn_file": None if vad_model is None else os.path.join(vad_model, "vad.mvn"),
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"vad_model_file": None if vad_model is None else os.path.join(vad_model, "vad.pb"),
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"vad_infer_config": None if vad_model is None else os.path.join(vad_model, "vad.yaml"),
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})
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# punc
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kwargs.update({
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"punc_model_file": None if punc_model is None else os.path.join(punc_model, "punc.pb"),
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"punc_infer_config": None if punc_model is None else os.path.join(punc_model, "punc.yaml"),
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})
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return model, vad_model, punc_model, kwargs
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name_maps_ms = {
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"paraformer-zh": "damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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"paraformer-zh-spk": "damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn",
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"paraformer-en": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020",
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"paraformer-en-spk": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020",
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"paraformer-zh-streaming": "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
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"fsmn-vad": "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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"ct-punc": "damo/punc_ct-transformer_cn-en-common-vocab471067-large",
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"fa-zh": "damo/speech_timestamp_prediction-v1-16k-offline",
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}
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def infer(task_name: str = "asr",
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model: str = None,
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# mode: str = None,
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vad_model: str = None,
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punc_model: str = None,
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model_hub: str = "ms",
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cache_dir: str = None,
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**kwargs,
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):
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model, vad_model, punc_model, kwargs = prepare_model(model, vad_model, punc_model, model_hub, cache_dir, **kwargs)
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if task_name == "asr":
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from funasr.bin.asr_inference_launch import inference_launch
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inference_pipeline = inference_launch(**kwargs)
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elif task_name == "":
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pipeline = 1
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elif task_name == "":
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pipeline = 2
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elif task_name == "":
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pipeline = 2
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def _infer_fn(input, **kwargs):
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data_type = kwargs.get('data_type', 'sound')
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data_path_and_name_and_type = [input, 'speech', data_type]
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raw_inputs = None
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if isinstance(input, torch.Tensor):
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input = input.numpy()
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if isinstance(input, np.ndarray):
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data_path_and_name_and_type = None
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raw_inputs = input
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return inference_pipeline(data_path_and_name_and_type, raw_inputs=raw_inputs, **kwargs)
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return _infer_fn
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if __name__ == '__main__':
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pass
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from funasr.bin.inference_cli import infer
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262
funasr/bin/argument.py
Normal file
262
funasr/bin/argument.py
Normal file
@ -0,0 +1,262 @@
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import sys
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.utils import config_argparse
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import argparse
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="ASR Decoding",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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# Note(kamo): Use '_' instead of '-' as separator.
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# '-' is confusing if written in yaml.
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parser.add_argument(
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"--log_level",
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type=lambda x: x.upper(),
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default="INFO",
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choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
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help="The verbose level of logging",
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)
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parser.add_argument("--output_dir", type=str, default=None)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=1,
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help="The number of gpus. 0 indicates CPU mode",
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)
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parser.add_argument(
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"--njob",
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type=int,
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default=1,
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help="The number of jobs for each gpu",
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)
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parser.add_argument(
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"--gpuid_list",
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type=str,
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default="",
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help="The visible gpus",
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)
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parser.add_argument("--seed", type=int, default=0, help="Random seed")
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parser.add_argument(
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"--dtype",
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default="float32",
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choices=["float16", "float32", "float64"],
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help="Data type",
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)
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parser.add_argument(
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"--num_workers",
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type=int,
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default=1,
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help="The number of workers used for DataLoader",
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)
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group = parser.add_argument_group("Input data related")
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group.add_argument(
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"--data_path_and_name_and_type",
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type=str2triple_str,
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required=False,
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action="append",
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)
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group.add_argument("--key_file", type=str_or_none)
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parser.add_argument(
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"--hotword",
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type=str_or_none,
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default=None,
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help="hotword file path or hotwords seperated by space"
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)
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group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
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group.add_argument(
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"--mc",
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type=bool,
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default=False,
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help="MultiChannel input",
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)
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group = parser.add_argument_group("The model configuration related")
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group.add_argument(
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"--vad_infer_config",
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type=str,
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help="VAD infer configuration",
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)
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group.add_argument(
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"--vad_model_file",
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type=str,
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help="VAD model parameter file",
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)
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group.add_argument(
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"--punc_infer_config",
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type=str,
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help="PUNC infer configuration",
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)
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group.add_argument(
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"--punc_model_file",
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type=str,
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help="PUNC model parameter file",
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)
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group.add_argument(
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"--cmvn_file",
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type=str,
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help="Global CMVN file",
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)
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group.add_argument(
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"--asr_train_config",
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type=str,
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help="ASR training configuration",
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)
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group.add_argument(
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"--asr_model_file",
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type=str,
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help="ASR model parameter file",
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)
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group.add_argument(
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"--sv_model_file",
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type=str,
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help="SV model parameter file",
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)
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group.add_argument(
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"--lm_train_config",
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type=str,
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help="LM training configuration",
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)
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group.add_argument(
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"--lm_file",
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type=str,
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help="LM parameter file",
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)
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group.add_argument(
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"--word_lm_train_config",
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type=str,
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help="Word LM training configuration",
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)
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group.add_argument(
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"--word_lm_file",
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type=str,
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help="Word LM parameter file",
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)
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group.add_argument(
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"--ngram_file",
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type=str,
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help="N-gram parameter file",
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)
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group.add_argument(
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"--model_tag",
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type=str,
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help="Pretrained model tag. If specify this option, *_train_config and "
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"*_file will be overwritten",
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)
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group.add_argument(
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"--beam_search_config",
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default={},
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help="The keyword arguments for transducer beam search.",
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)
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group = parser.add_argument_group("Beam-search related")
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group.add_argument(
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"--batch_size",
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type=int,
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default=1,
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help="The batch size for inference",
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)
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group.add_argument("--nbest", type=int, default=5, help="Output N-best hypotheses")
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group.add_argument("--beam_size", type=int, default=20, help="Beam size")
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group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")
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group.add_argument(
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"--maxlenratio",
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type=float,
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default=0.0,
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help="Input length ratio to obtain max output length. "
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"If maxlenratio=0.0 (default), it uses a end-detect "
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"function "
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"to automatically find maximum hypothesis lengths."
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"If maxlenratio<0.0, its absolute value is interpreted"
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"as a constant max output length",
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)
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group.add_argument(
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"--minlenratio",
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type=float,
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default=0.0,
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help="Input length ratio to obtain min output length",
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)
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group.add_argument(
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"--ctc_weight",
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type=float,
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default=0.0,
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help="CTC weight in joint decoding",
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)
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group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight")
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group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight")
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group.add_argument("--streaming", type=str2bool, default=False)
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group.add_argument("--fake_streaming", type=str2bool, default=False)
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group.add_argument("--full_utt", type=str2bool, default=False)
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group.add_argument("--chunk_size", type=int, default=16)
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group.add_argument("--left_context", type=int, default=16)
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group.add_argument("--right_context", type=int, default=0)
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group.add_argument(
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"--display_partial_hypotheses",
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type=bool,
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default=False,
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help="Whether to display partial hypotheses during chunk-by-chunk inference.",
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)
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group = parser.add_argument_group("Dynamic quantization related")
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group.add_argument(
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"--quantize_asr_model",
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type=bool,
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default=False,
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help="Apply dynamic quantization to ASR model.",
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)
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group.add_argument(
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"--quantize_modules",
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nargs="*",
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default=None,
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help="""Module names to apply dynamic quantization on.
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The module names are provided as a list, where each name is separated
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by a comma (e.g.: --quantize-config=[Linear,LSTM,GRU]).
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Each specified name should be an attribute of 'torch.nn', e.g.:
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torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""",
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)
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group.add_argument(
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"--quantize_dtype",
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type=str,
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default="qint8",
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choices=["float16", "qint8"],
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help="Dtype for dynamic quantization.",
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)
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group = parser.add_argument_group("Text converter related")
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group.add_argument(
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"--token_type",
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type=str_or_none,
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default=None,
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choices=["char", "bpe", None],
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help="The token type for ASR model. "
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"If not given, refers from the training args",
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)
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group.add_argument(
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"--bpemodel",
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type=str_or_none,
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default=None,
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help="The model path of sentencepiece. "
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"If not given, refers from the training args",
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)
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group.add_argument("--token_num_relax", type=int, default=1, help="")
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group.add_argument("--decoding_ind", type=int, default=0, help="")
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group.add_argument("--decoding_mode", type=str, default="model1", help="")
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group.add_argument(
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"--ctc_weight2",
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type=float,
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default=0.0,
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help="CTC weight in joint decoding",
|
||||
)
|
||||
return parser
|
||||
@ -675,11 +675,13 @@ def inference_paraformer_vad_punc(
|
||||
beg_idx = end_idx
|
||||
batch = {"speech": speech_j, "speech_lengths": speech_lengths_j}
|
||||
batch = to_device(batch, device=device)
|
||||
# print("batch: ", speech_j.shape[0])
|
||||
|
||||
beg_asr = time.time()
|
||||
results = speech2text(**batch)
|
||||
end_asr = time.time()
|
||||
# print("time cost asr: ", end_asr - beg_asr)
|
||||
if speech2text.device != "cpu":
|
||||
print("batch: ", speech_j.shape[0])
|
||||
print("time cost asr: ", end_asr - beg_asr)
|
||||
|
||||
if len(results) < 1:
|
||||
results = [["", [], [], [], [], [], []]]
|
||||
@ -2218,259 +2220,9 @@ def inference_launch(**kwargs):
|
||||
logging.info("Unknown decoding mode: {}".format(mode))
|
||||
return None
|
||||
|
||||
|
||||
def get_parser():
|
||||
parser = config_argparse.ArgumentParser(
|
||||
description="ASR Decoding",
|
||||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||||
)
|
||||
|
||||
# Note(kamo): Use '_' instead of '-' as separator.
|
||||
# '-' is confusing if written in yaml.
|
||||
parser.add_argument(
|
||||
"--log_level",
|
||||
type=lambda x: x.upper(),
|
||||
default="INFO",
|
||||
choices=("CRITICAL", "ERROR", "WARNING", "INFO", "DEBUG", "NOTSET"),
|
||||
help="The verbose level of logging",
|
||||
)
|
||||
|
||||
parser.add_argument("--output_dir", type=str, required=True)
|
||||
parser.add_argument(
|
||||
"--ngpu",
|
||||
type=int,
|
||||
default=0,
|
||||
help="The number of gpus. 0 indicates CPU mode",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--njob",
|
||||
type=int,
|
||||
default=1,
|
||||
help="The number of jobs for each gpu",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--gpuid_list",
|
||||
type=str,
|
||||
default="",
|
||||
help="The visible gpus",
|
||||
)
|
||||
parser.add_argument("--seed", type=int, default=0, help="Random seed")
|
||||
parser.add_argument(
|
||||
"--dtype",
|
||||
default="float32",
|
||||
choices=["float16", "float32", "float64"],
|
||||
help="Data type",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--num_workers",
|
||||
type=int,
|
||||
default=1,
|
||||
help="The number of workers used for DataLoader",
|
||||
)
|
||||
|
||||
group = parser.add_argument_group("Input data related")
|
||||
group.add_argument(
|
||||
"--data_path_and_name_and_type",
|
||||
type=str2triple_str,
|
||||
required=True,
|
||||
action="append",
|
||||
)
|
||||
group.add_argument("--key_file", type=str_or_none)
|
||||
parser.add_argument(
|
||||
"--hotword",
|
||||
type=str_or_none,
|
||||
default=None,
|
||||
help="hotword file path or hotwords seperated by space"
|
||||
)
|
||||
group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
|
||||
group.add_argument(
|
||||
"--mc",
|
||||
type=bool,
|
||||
default=False,
|
||||
help="MultiChannel input",
|
||||
)
|
||||
|
||||
group = parser.add_argument_group("The model configuration related")
|
||||
group.add_argument(
|
||||
"--vad_infer_config",
|
||||
type=str,
|
||||
help="VAD infer configuration",
|
||||
)
|
||||
group.add_argument(
|
||||
"--vad_model_file",
|
||||
type=str,
|
||||
help="VAD model parameter file",
|
||||
)
|
||||
group.add_argument(
|
||||
"--punc_infer_config",
|
||||
type=str,
|
||||
help="PUNC infer configuration",
|
||||
)
|
||||
group.add_argument(
|
||||
"--punc_model_file",
|
||||
type=str,
|
||||
help="PUNC model parameter file",
|
||||
)
|
||||
group.add_argument(
|
||||
"--cmvn_file",
|
||||
type=str,
|
||||
help="Global CMVN file",
|
||||
)
|
||||
group.add_argument(
|
||||
"--asr_train_config",
|
||||
type=str,
|
||||
help="ASR training configuration",
|
||||
)
|
||||
group.add_argument(
|
||||
"--asr_model_file",
|
||||
type=str,
|
||||
help="ASR model parameter file",
|
||||
)
|
||||
group.add_argument(
|
||||
"--sv_model_file",
|
||||
type=str,
|
||||
help="SV model parameter file",
|
||||
)
|
||||
group.add_argument(
|
||||
"--lm_train_config",
|
||||
type=str,
|
||||
help="LM training configuration",
|
||||
)
|
||||
group.add_argument(
|
||||
"--lm_file",
|
||||
type=str,
|
||||
help="LM parameter file",
|
||||
)
|
||||
group.add_argument(
|
||||
"--word_lm_train_config",
|
||||
type=str,
|
||||
help="Word LM training configuration",
|
||||
)
|
||||
group.add_argument(
|
||||
"--word_lm_file",
|
||||
type=str,
|
||||
help="Word LM parameter file",
|
||||
)
|
||||
group.add_argument(
|
||||
"--ngram_file",
|
||||
type=str,
|
||||
help="N-gram parameter file",
|
||||
)
|
||||
group.add_argument(
|
||||
"--model_tag",
|
||||
type=str,
|
||||
help="Pretrained model tag. If specify this option, *_train_config and "
|
||||
"*_file will be overwritten",
|
||||
)
|
||||
group.add_argument(
|
||||
"--beam_search_config",
|
||||
default={},
|
||||
help="The keyword arguments for transducer beam search.",
|
||||
)
|
||||
|
||||
group = parser.add_argument_group("Beam-search related")
|
||||
group.add_argument(
|
||||
"--batch_size",
|
||||
type=int,
|
||||
default=1,
|
||||
help="The batch size for inference",
|
||||
)
|
||||
group.add_argument("--nbest", type=int, default=5, help="Output N-best hypotheses")
|
||||
group.add_argument("--beam_size", type=int, default=20, help="Beam size")
|
||||
group.add_argument("--penalty", type=float, default=0.0, help="Insertion penalty")
|
||||
group.add_argument(
|
||||
"--maxlenratio",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="Input length ratio to obtain max output length. "
|
||||
"If maxlenratio=0.0 (default), it uses a end-detect "
|
||||
"function "
|
||||
"to automatically find maximum hypothesis lengths."
|
||||
"If maxlenratio<0.0, its absolute value is interpreted"
|
||||
"as a constant max output length",
|
||||
)
|
||||
group.add_argument(
|
||||
"--minlenratio",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="Input length ratio to obtain min output length",
|
||||
)
|
||||
group.add_argument(
|
||||
"--ctc_weight",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="CTC weight in joint decoding",
|
||||
)
|
||||
group.add_argument("--lm_weight", type=float, default=1.0, help="RNNLM weight")
|
||||
group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight")
|
||||
group.add_argument("--streaming", type=str2bool, default=False)
|
||||
group.add_argument("--fake_streaming", type=str2bool, default=False)
|
||||
group.add_argument("--full_utt", type=str2bool, default=False)
|
||||
group.add_argument("--chunk_size", type=int, default=16)
|
||||
group.add_argument("--left_context", type=int, default=16)
|
||||
group.add_argument("--right_context", type=int, default=0)
|
||||
group.add_argument(
|
||||
"--display_partial_hypotheses",
|
||||
type=bool,
|
||||
default=False,
|
||||
help="Whether to display partial hypotheses during chunk-by-chunk inference.",
|
||||
)
|
||||
|
||||
group = parser.add_argument_group("Dynamic quantization related")
|
||||
group.add_argument(
|
||||
"--quantize_asr_model",
|
||||
type=bool,
|
||||
default=False,
|
||||
help="Apply dynamic quantization to ASR model.",
|
||||
)
|
||||
group.add_argument(
|
||||
"--quantize_modules",
|
||||
nargs="*",
|
||||
default=None,
|
||||
help="""Module names to apply dynamic quantization on.
|
||||
The module names are provided as a list, where each name is separated
|
||||
by a comma (e.g.: --quantize-config=[Linear,LSTM,GRU]).
|
||||
Each specified name should be an attribute of 'torch.nn', e.g.:
|
||||
torch.nn.Linear, torch.nn.LSTM, torch.nn.GRU, ...""",
|
||||
)
|
||||
group.add_argument(
|
||||
"--quantize_dtype",
|
||||
type=str,
|
||||
default="qint8",
|
||||
choices=["float16", "qint8"],
|
||||
help="Dtype for dynamic quantization.",
|
||||
)
|
||||
|
||||
group = parser.add_argument_group("Text converter related")
|
||||
group.add_argument(
|
||||
"--token_type",
|
||||
type=str_or_none,
|
||||
default=None,
|
||||
choices=["char", "bpe", None],
|
||||
help="The token type for ASR model. "
|
||||
"If not given, refers from the training args",
|
||||
)
|
||||
group.add_argument(
|
||||
"--bpemodel",
|
||||
type=str_or_none,
|
||||
default=None,
|
||||
help="The model path of sentencepiece. "
|
||||
"If not given, refers from the training args",
|
||||
)
|
||||
group.add_argument("--token_num_relax", type=int, default=1, help="")
|
||||
group.add_argument("--decoding_ind", type=int, default=0, help="")
|
||||
group.add_argument("--decoding_mode", type=str, default="model1", help="")
|
||||
group.add_argument(
|
||||
"--ctc_weight2",
|
||||
type=float,
|
||||
default=0.0,
|
||||
help="CTC weight in joint decoding",
|
||||
)
|
||||
return parser
|
||||
|
||||
|
||||
def main(cmd=None):
|
||||
print(get_commandline_args(), file=sys.stderr)
|
||||
from funasr.bin.argument import get_parser
|
||||
parser = get_parser()
|
||||
parser.add_argument(
|
||||
"--mode",
|
||||
|
||||
139
funasr/bin/inference_cli.py
Normal file
139
funasr/bin/inference_cli.py
Normal file
@ -0,0 +1,139 @@
|
||||
#!/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 os
|
||||
|
||||
import logging
|
||||
import torch
|
||||
import numpy as np
|
||||
from funasr.utils.download_and_prepare_model import prepare_model
|
||||
|
||||
from funasr.utils.types import str2bool
|
||||
|
||||
def infer(task_name: str = "asr",
|
||||
model: str = None,
|
||||
# mode: str = None,
|
||||
vad_model: str = None,
|
||||
disable_vad: bool = False,
|
||||
punc_model: str = None,
|
||||
disable_punc: bool = False,
|
||||
model_hub: str = "ms",
|
||||
cache_dir: str = None,
|
||||
**kwargs,
|
||||
):
|
||||
|
||||
# set logging messages
|
||||
logging.basicConfig(
|
||||
level=logging.ERROR,
|
||||
)
|
||||
|
||||
model, vad_model, punc_model, kwargs = prepare_model(model, vad_model, punc_model, model_hub, cache_dir, **kwargs)
|
||||
if task_name == "asr":
|
||||
from funasr.bin.asr_inference_launch import inference_launch
|
||||
|
||||
inference_pipeline = inference_launch(**kwargs)
|
||||
elif task_name == "":
|
||||
pipeline = 1
|
||||
elif task_name == "":
|
||||
pipeline = 2
|
||||
elif task_name == "":
|
||||
pipeline = 2
|
||||
|
||||
def _infer_fn(input, **kwargs):
|
||||
data_type = kwargs.get('data_type', 'sound')
|
||||
data_path_and_name_and_type = [input, 'speech', data_type]
|
||||
raw_inputs = None
|
||||
if isinstance(input, torch.Tensor):
|
||||
input = input.numpy()
|
||||
if isinstance(input, np.ndarray):
|
||||
data_path_and_name_and_type = None
|
||||
raw_inputs = input
|
||||
|
||||
return inference_pipeline(data_path_and_name_and_type, raw_inputs=raw_inputs, **kwargs)
|
||||
|
||||
return _infer_fn
|
||||
|
||||
|
||||
def main(cmd=None):
|
||||
# print(get_commandline_args(), file=sys.stderr)
|
||||
from funasr.bin.argument import get_parser
|
||||
|
||||
parser = get_parser()
|
||||
parser.add_argument('input', help='input file to transcribe')
|
||||
parser.add_argument(
|
||||
"--task_name",
|
||||
type=str,
|
||||
default="asr",
|
||||
help="The decoding mode",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-m",
|
||||
"--model",
|
||||
type=str,
|
||||
default="paraformer-zh",
|
||||
help="The asr mode name",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-v",
|
||||
"--vad_model",
|
||||
type=str,
|
||||
default="fsmn-vad",
|
||||
help="vad model name",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-dv",
|
||||
"--disable_vad",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-p",
|
||||
"--punc_model",
|
||||
type=str,
|
||||
default="ct-punc",
|
||||
help="",
|
||||
)
|
||||
parser.add_argument(
|
||||
"-dp",
|
||||
"--disable_punc",
|
||||
type=str2bool,
|
||||
default=False,
|
||||
help="",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--batch_size_token",
|
||||
type=int,
|
||||
default=5000,
|
||||
help="",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--batch_size_token_threshold_s",
|
||||
type=int,
|
||||
default=35,
|
||||
help="",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--max_single_segment_time",
|
||||
type=int,
|
||||
default=5000,
|
||||
help="",
|
||||
)
|
||||
args = parser.parse_args(cmd)
|
||||
kwargs = vars(args)
|
||||
|
||||
# set logging messages
|
||||
logging.basicConfig(
|
||||
level=logging.ERROR,
|
||||
)
|
||||
logging.info("Decoding args: {}".format(kwargs))
|
||||
|
||||
# kwargs["ncpu"] = 2 #os.cpu_count()
|
||||
kwargs.pop("data_path_and_name_and_type")
|
||||
print("args: {}".format(kwargs))
|
||||
p = infer(**kwargs)
|
||||
|
||||
res = p(**kwargs)
|
||||
print(res)
|
||||
93
funasr/utils/download_and_prepare_model.py
Normal file
93
funasr/utils/download_and_prepare_model.py
Normal file
@ -0,0 +1,93 @@
|
||||
|
||||
import os
|
||||
from pathlib import Path
|
||||
import logging
|
||||
|
||||
name_maps_ms = {
|
||||
"paraformer-zh": "damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
|
||||
"paraformer-zh-spk": "damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn",
|
||||
"paraformer-en": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020",
|
||||
"paraformer-en-spk": "damo/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020",
|
||||
"paraformer-zh-streaming": "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online",
|
||||
"fsmn-vad": "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
|
||||
"ct-punc": "damo/punc_ct-transformer_cn-en-common-vocab471067-large",
|
||||
"fa-zh": "damo/speech_timestamp_prediction-v1-16k-offline",
|
||||
}
|
||||
|
||||
def prepare_model(
|
||||
model: str = None,
|
||||
# mode: str = None,
|
||||
vad_model: str = None,
|
||||
punc_model: str = None,
|
||||
model_hub: str = "ms",
|
||||
cache_dir: str = None,
|
||||
**kwargs,
|
||||
):
|
||||
if not Path(model).exists():
|
||||
if model_hub == "ms" or model_hub == "modelscope":
|
||||
from modelscope.utils.logger import get_logger
|
||||
|
||||
logger = get_logger(log_level=logging.CRITICAL)
|
||||
logger.setLevel(logging.CRITICAL)
|
||||
try:
|
||||
from modelscope.hub.snapshot_download import snapshot_download as download_tool
|
||||
model = name_maps_ms[model] if model is not None else None
|
||||
vad_model = name_maps_ms[vad_model] if vad_model is not None else None
|
||||
punc_model = name_maps_ms[punc_model] if punc_model is not None else None
|
||||
except:
|
||||
raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" \
|
||||
"\npip3 install -U modelscope\n" \
|
||||
"For the users in China, you could install with the command:\n" \
|
||||
"\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple"
|
||||
|
||||
try:
|
||||
model = download_tool(model, cache_dir=cache_dir, revision=kwargs.get("revision", None))
|
||||
print("asr model have been downloaded to: {}".format(model))
|
||||
except:
|
||||
raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
|
||||
model)
|
||||
|
||||
elif model_hub == "hf" or model_hub == "huggingface":
|
||||
download_tool = 0
|
||||
else:
|
||||
raise "model_hub must be on of ms or hf, but get {}".format(model_hub)
|
||||
|
||||
|
||||
if vad_model is not None and not Path(vad_model).exists():
|
||||
vad_model = download_tool(vad_model, cache_dir=cache_dir)
|
||||
print("vad_model have been downloaded to: {}".format(vad_model))
|
||||
if punc_model is not None and not Path(punc_model).exists():
|
||||
punc_model = download_tool(punc_model, cache_dir=cache_dir)
|
||||
print("punc_model have been downloaded to: {}".format(punc_model))
|
||||
|
||||
# asr
|
||||
kwargs.update({"cmvn_file": None if model is None else os.path.join(model, "am.mvn"),
|
||||
"asr_model_file": None if model is None else os.path.join(model, "model.pb"),
|
||||
"asr_train_config": None if model is None else os.path.join(model, "config.yaml"),
|
||||
})
|
||||
mode = kwargs.get("mode", None)
|
||||
if mode is None:
|
||||
import json
|
||||
json_file = os.path.join(model, 'configuration.json')
|
||||
with open(json_file, 'r') as f:
|
||||
config_data = json.load(f)
|
||||
if config_data['task'] == "punctuation":
|
||||
mode = config_data['model']['punc_model_config']['mode']
|
||||
else:
|
||||
mode = config_data['model']['model_config']['mode']
|
||||
if vad_model is not None and "vad" not in mode:
|
||||
mode = "paraformer_vad"
|
||||
kwargs["mode"] = mode
|
||||
# vad
|
||||
kwargs.update({"vad_cmvn_file": None if vad_model is None else os.path.join(vad_model, "vad.mvn"),
|
||||
"vad_model_file": None if vad_model is None else os.path.join(vad_model, "vad.pb"),
|
||||
"vad_infer_config": None if vad_model is None else os.path.join(vad_model, "vad.yaml"),
|
||||
})
|
||||
# punc
|
||||
kwargs.update({
|
||||
"punc_model_file": None if punc_model is None else os.path.join(punc_model, "punc.pb"),
|
||||
"punc_infer_config": None if punc_model is None else os.path.join(punc_model, "punc.yaml"),
|
||||
})
|
||||
|
||||
|
||||
return model, vad_model, punc_model, kwargs
|
||||
@ -1 +1 @@
|
||||
0.8.4
|
||||
0.8.5
|
||||
|
||||
Loading…
Reference in New Issue
Block a user