"""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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