mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
98abc0e5ac
* update * update setup * update setup * update setup * update setup * update setup * update setup * update * update * update setup
93 lines
3.3 KiB
Python
93 lines
3.3 KiB
Python
#!/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 logging
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from pathlib import Path
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from typing import Union
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import numpy as np
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import torch
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from funasr.build_utils.build_model_from_file import build_model_from_file
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from funasr.models.frontend.wav_frontend import WavFrontend
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from funasr.text.token_id_converter import TokenIDConverter
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from funasr.torch_utils.device_funcs import to_device
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class Speech2Timestamp:
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def __init__(
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self,
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timestamp_infer_config: Union[Path, str] = None,
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timestamp_model_file: Union[Path, str] = None,
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timestamp_cmvn_file: Union[Path, str] = None,
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device: str = "cpu",
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dtype: str = "float32",
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**kwargs,
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):
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# 1. Build ASR model
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tp_model, tp_train_args = build_model_from_file(
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timestamp_infer_config, timestamp_model_file, cmvn_file=None, device=device, task_name="asr", mode="tp"
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)
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if 'cuda' in device:
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tp_model = tp_model.cuda() # force model to cuda
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frontend = None
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if tp_train_args.frontend is not None:
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frontend = WavFrontend(cmvn_file=timestamp_cmvn_file, **tp_train_args.frontend_conf)
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logging.info("tp_model: {}".format(tp_model))
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logging.info("tp_train_args: {}".format(tp_train_args))
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tp_model.to(dtype=getattr(torch, dtype)).eval()
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logging.info(f"Decoding device={device}, dtype={dtype}")
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self.tp_model = tp_model
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self.tp_train_args = tp_train_args
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token_list = self.tp_model.token_list
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self.converter = TokenIDConverter(token_list=token_list)
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self.device = device
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self.dtype = dtype
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self.frontend = frontend
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self.encoder_downsampling_factor = 1
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if tp_train_args.encoder_conf["input_layer"] == "conv2d":
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self.encoder_downsampling_factor = 4
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@torch.no_grad()
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def __call__(
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self,
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speech: Union[torch.Tensor, np.ndarray],
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speech_lengths: Union[torch.Tensor, np.ndarray] = None,
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text_lengths: Union[torch.Tensor, np.ndarray] = None
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):
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# Input as audio signal
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if isinstance(speech, np.ndarray):
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speech = torch.tensor(speech)
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if self.frontend is not None:
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feats, feats_len = self.frontend.forward(speech, speech_lengths)
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feats = to_device(feats, device=self.device)
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feats_len = feats_len.int()
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self.tp_model.frontend = None
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else:
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feats = speech
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feats_len = speech_lengths
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# lfr_factor = max(1, (feats.size()[-1]//80)-1)
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batch = {"speech": feats, "speech_lengths": feats_len}
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# a. To device
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batch = to_device(batch, device=self.device)
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# b. Forward Encoder
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enc, enc_len = self.tp_model.encode(**batch)
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if isinstance(enc, tuple):
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enc = enc[0]
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# c. Forward Predictor
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_, _, us_alphas, us_peaks = self.tp_model.calc_predictor_timestamp(enc, enc_len,
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text_lengths.to(self.device) + 1)
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return us_alphas, us_peaks
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