#!/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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Hypothesis,
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ONNXRuntimeError,
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OrtInferSession,
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TokenIDConverter,
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get_logger,
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read_yaml,
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)
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from .utils.sentencepiece_tokenizer import SentencepiecesTokenizer
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from .utils.frontend import WavFrontend
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logging = get_logger()
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class SenseVoiceSmall:
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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 not Path(model_dir).exists():
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try:
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from modelscope.hub.snapshot_download import snapshot_download
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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" "\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"
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try:
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model_dir = snapshot_download(model_dir, cache_dir=cache_dir)
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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_dir
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)
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model_file = os.path.join(model_dir, "model.onnx")
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if quantize:
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model_file = os.path.join(model_dir, "model_quant.onnx")
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if not os.path.exists(model_file):
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print(".onnx does not exist, begin to export onnx")
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try:
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from funasr import AutoModel
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except:
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raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" "\npip3 install -U funasr\n" "For the users in China, you could install with the command:\n" "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple"
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model = AutoModel(model=model_dir)
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model_dir = model.export(type="onnx", quantize=quantize, **kwargs)
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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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self.tokenizer = SentencepiecesTokenizer(
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bpemodel=os.path.join(model_dir, "chn_jpn_yue_eng_ko_spectok.bpe.model")
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)
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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 = OrtInferSession(
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model_file, device_id, intra_op_num_threads=intra_op_num_threads
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)
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self.batch_size = batch_size
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self.blank_id = 0
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self.lid_dict = {"auto": 0, "zh": 3, "en": 4, "yue": 7, "ja": 11, "ko": 12, "nospeech": 13}
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self.lid_int_dict = {24884: 3, 24885: 4, 24888: 7, 24892: 11, 24896: 12, 24992: 13}
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self.textnorm_dict = {"withitn": 14, "woitn": 15}
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self.textnorm_int_dict = {25016: 14, 25017: 15}
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def _get_lid(self, lid):
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if lid in list(self.lid_dict.keys()):
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return self.lid_dict[lid]
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else:
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raise ValueError(
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f"The language {l} is not in {list(self.lid_dict.keys())}"
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)
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def _get_tnid(self, tnid):
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if tnid in list(self.textnorm_dict.keys()):
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return self.textnorm_dict[tnid]
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else:
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raise ValueError(
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f"The textnorm {tnid} is not in {list(self.textnorm_dict.keys())}"
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)
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def read_tags(self, language_input, textnorm_input):
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# handle language
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if isinstance(language_input, list):
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language_list = []
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for l in language_input:
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language_list.append(self._get_lid(l))
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elif isinstance(language_input, str):
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# if is existing file
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if os.path.exists(language_input):
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language_file = open(language_input, "r").readlines()
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language_list = [
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self._get_lid(l.strip())
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for l in language_file
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]
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else:
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language_list = [self._get_lid(language_input)]
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else:
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raise ValueError(
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f"Unsupported type {type(language_input)} for language_input"
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)
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# handle textnorm
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if isinstance(textnorm_input, list):
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textnorm_list = []
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for tn in textnorm_input:
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textnorm_list.append(self._get_tnid(tn))
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elif isinstance(textnorm_input, str):
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# if is existing file
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if os.path.exists(textnorm_input):
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textnorm_file = open(textnorm_input, "r").readlines()
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textnorm_list = [
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self._get_tnid(tn.strip())
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for tn in textnorm_file
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]
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else:
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textnorm_list = [self._get_tnid(textnorm_input)]
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else:
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raise ValueError(
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f"Unsupported type {type(textnorm_input)} for textnorm_input"
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)
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return language_list, textnorm_list
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def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs):
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language_input = kwargs.get("language", "auto")
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textnorm_input = kwargs.get("textnorm", "woitn")
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language_list, textnorm_list = self.read_tags(language_input, textnorm_input)
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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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assert len(language_list) == 1 or len(language_list) == waveform_nums, \
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"length of parsed language list should be 1 or equal to the number of waveforms"
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assert len(textnorm_list) == 1 or len(textnorm_list) == waveform_nums, \
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"length of parsed textnorm list should be 1 or equal to the number of waveforms"
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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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_language_list = language_list[beg_idx:end_idx]
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_textnorm_list = textnorm_list[beg_idx:end_idx]
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if not len(_language_list):
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_language_list = [language_list[0]]
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_textnorm_list = [textnorm_list[0]]
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B = feats.shape[0]
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if len(_language_list) == 1 and B != 1:
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_language_list = _language_list * B
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if len(_textnorm_list) == 1 and B != 1:
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_textnorm_list = _textnorm_list * B
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ctc_logits, encoder_out_lens = self.infer(
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feats,
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feats_len,
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np.array(_language_list, dtype=np.int32),
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np.array(_textnorm_list, dtype=np.int32),
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)
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for b in range(feats.shape[0]):
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# back to torch.Tensor
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if isinstance(ctc_logits, np.ndarray):
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ctc_logits = torch.from_numpy(ctc_logits).float()
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# support batch_size=1 only currently
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x = ctc_logits[b, : encoder_out_lens[b].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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asr_res.append(self.tokenizer.decode(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 convert_to_wav(input_path, output_path):
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from pydub import AudioSegment
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try:
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audio = AudioSegment.from_mp3(input_path)
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audio.export(output_path, format="wav")
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print("音频文件为mp3格式，已转换为wav格式")
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except Exception as e:
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print(f"转换失败:{e}")
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def load_wav(path: str) -> np.ndarray:
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if not path.lower().endswith('.wav'):
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import os
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input_path = path
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path = os.path.splitext(path)[0]+'.wav'
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convert_to_wav(input_path,path) #将mp3格式转换成wav格式
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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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if speech is None or speech.size == 0:
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print("detected speech size {speech.size}")
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raise ValueError("Empty speech detected, skipping this 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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def infer(
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self,
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feats: np.ndarray,
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feats_len: np.ndarray,
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language: np.ndarray,
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textnorm: np.ndarray,
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) -> Tuple[np.ndarray, np.ndarray]:
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outputs = self.ort_infer([feats, feats_len, language, textnorm])
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return outputs
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