#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/FunAudioLLM/SenseVoice). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import torch
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import os.path
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import librosa
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import numpy as np
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from pathlib import Path
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from typing import List, Union, Tuple
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from .utils.utils import (
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CharTokenizer,
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get_logger,
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read_yaml,
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)
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from .utils.frontend import WavFrontend
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logging = get_logger()
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class SenseVoiceSmallTorchScript:
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
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https://arxiv.org/abs/2206.08317
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"""
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def __init__(
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self,
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model_dir: Union[str, Path] = None,
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batch_size: int = 1,
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device_id: Union[str, int] = "-1",
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plot_timestamp_to: str = "",
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quantize: bool = False,
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intra_op_num_threads: int = 4,
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cache_dir: str = None,
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**kwargs,
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):
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if quantize:
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model_file = os.path.join(model_dir, "model_quant.torchscript")
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else:
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model_file = os.path.join(model_dir, "model.torchscript")
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config_file = os.path.join(model_dir, "config.yaml")
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cmvn_file = os.path.join(model_dir, "am.mvn")
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config = read_yaml(config_file)
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# token_list = os.path.join(model_dir, "tokens.json")
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# with open(token_list, "r", encoding="utf-8") as f:
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# token_list = json.load(f)
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# self.converter = TokenIDConverter(token_list)
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self.tokenizer = CharTokenizer()
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config["frontend_conf"]['cmvn_file'] = cmvn_file
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self.frontend = WavFrontend(**config["frontend_conf"])
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self.ort_infer = torch.jit.load(model_file)
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self.batch_size = batch_size
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self.blank_id = 0
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def __call__(self,
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wav_content: Union[str, np.ndarray, List[str]],
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language: List,
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textnorm: List,
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tokenizer=None,
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**kwargs) -> List:
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waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
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waveform_nums = len(waveform_list)
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asr_res = []
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for beg_idx in range(0, waveform_nums, self.batch_size):
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end_idx = min(waveform_nums, beg_idx + self.batch_size)
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feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
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ctc_logits, encoder_out_lens = self.ort_infer(torch.Tensor(feats),
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torch.Tensor(feats_len),
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torch.tensor(language),
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torch.tensor(textnorm)
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)
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# support batch_size=1 only currently
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x = ctc_logits[0, : encoder_out_lens[0].item(), :]
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yseq = x.argmax(dim=-1)
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yseq = torch.unique_consecutive(yseq, dim=-1)
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mask = yseq != self.blank_id
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token_int = yseq[mask].tolist()
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if tokenizer is not None:
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asr_res.append(tokenizer.tokens2text(token_int))
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else:
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asr_res.append(token_int)
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return asr_res
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def load_data(self, wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
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def load_wav(path: str) -> np.ndarray:
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waveform, _ = librosa.load(path, sr=fs)
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return waveform
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if isinstance(wav_content, np.ndarray):
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return [wav_content]
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if isinstance(wav_content, str):
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return [load_wav(wav_content)]
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if isinstance(wav_content, list):
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return [load_wav(path) for path in wav_content]
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raise TypeError(f"The type of {wav_content} is not in [str, np.ndarray, list]")
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def extract_feat(self, waveform_list: List[np.ndarray]) -> Tuple[np.ndarray, np.ndarray]:
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feats, feats_len = [], []
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for waveform in waveform_list:
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speech, _ = self.frontend.fbank(waveform)
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feat, feat_len = self.frontend.lfr_cmvn(speech)
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feats.append(feat)
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feats_len.append(feat_len)
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feats = self.pad_feats(feats, np.max(feats_len))
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feats_len = np.array(feats_len).astype(np.int32)
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return feats, feats_len
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@staticmethod
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def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
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def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
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pad_width = ((0, max_feat_len - cur_len), (0, 0))
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return np.pad(feat, pad_width, "constant", constant_values=0)
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feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
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feats = np.array(feat_res).astype(np.float32)
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return feats
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