mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
a76f15c785
* 添加了对音频文件扩展名是否为.mp3的补丁，是mp3格式则转化为wav格式 * 增加检测音频文件是否为mp3格式的补丁 * 完善对音频文件后缀名的检查，若文件后缀不是.wav，则转化为wav * 增加音频文件后缀名检查；音频文件无效时抛出错误
266 lines
10 KiB
Python
266 lines
10 KiB
Python
#!/usr/bin/env python3
|
||
# -*- encoding: utf-8 -*-
|
||
# Copyright FunASR (https://github.com/FunAudioLLM/SenseVoice). All Rights Reserved.
|
||
# MIT License (https://opensource.org/licenses/MIT)
|
||
|
||
|
||
import torch
|
||
import os.path
|
||
import librosa
|
||
import numpy as np
|
||
from pathlib import Path
|
||
from typing import List, Union, Tuple
|
||
|
||
from .utils.utils import (
|
||
CharTokenizer,
|
||
Hypothesis,
|
||
ONNXRuntimeError,
|
||
OrtInferSession,
|
||
TokenIDConverter,
|
||
get_logger,
|
||
read_yaml,
|
||
)
|
||
from .utils.sentencepiece_tokenizer import SentencepiecesTokenizer
|
||
from .utils.frontend import WavFrontend
|
||
|
||
logging = get_logger()
|
||
|
||
|
||
class SenseVoiceSmall:
|
||
"""
|
||
Author: Speech Lab of DAMO Academy, Alibaba Group
|
||
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
|
||
https://arxiv.org/abs/2206.08317
|
||
"""
|
||
|
||
def __init__(
|
||
self,
|
||
model_dir: Union[str, Path] = None,
|
||
batch_size: int = 1,
|
||
device_id: Union[str, int] = "-1",
|
||
plot_timestamp_to: str = "",
|
||
quantize: bool = False,
|
||
intra_op_num_threads: int = 4,
|
||
cache_dir: str = None,
|
||
**kwargs,
|
||
):
|
||
|
||
if not Path(model_dir).exists():
|
||
try:
|
||
from modelscope.hub.snapshot_download import snapshot_download
|
||
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_dir = snapshot_download(model_dir, cache_dir=cache_dir)
|
||
except:
|
||
raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
|
||
model_dir
|
||
)
|
||
|
||
model_file = os.path.join(model_dir, "model.onnx")
|
||
if quantize:
|
||
model_file = os.path.join(model_dir, "model_quant.onnx")
|
||
if not os.path.exists(model_file):
|
||
print(".onnx does not exist, begin to export onnx")
|
||
try:
|
||
from funasr import AutoModel
|
||
except:
|
||
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"
|
||
|
||
model = AutoModel(model=model_dir)
|
||
model_dir = model.export(type="onnx", quantize=quantize, **kwargs)
|
||
|
||
config_file = os.path.join(model_dir, "config.yaml")
|
||
cmvn_file = os.path.join(model_dir, "am.mvn")
|
||
config = read_yaml(config_file)
|
||
|
||
self.tokenizer = SentencepiecesTokenizer(
|
||
bpemodel=os.path.join(model_dir, "chn_jpn_yue_eng_ko_spectok.bpe.model")
|
||
)
|
||
config["frontend_conf"]["cmvn_file"] = cmvn_file
|
||
self.frontend = WavFrontend(**config["frontend_conf"])
|
||
self.ort_infer = OrtInferSession(
|
||
model_file, device_id, intra_op_num_threads=intra_op_num_threads
|
||
)
|
||
self.batch_size = batch_size
|
||
self.blank_id = 0
|
||
self.lid_dict = {"auto": 0, "zh": 3, "en": 4, "yue": 7, "ja": 11, "ko": 12, "nospeech": 13}
|
||
self.lid_int_dict = {24884: 3, 24885: 4, 24888: 7, 24892: 11, 24896: 12, 24992: 13}
|
||
self.textnorm_dict = {"withitn": 14, "woitn": 15}
|
||
self.textnorm_int_dict = {25016: 14, 25017: 15}
|
||
|
||
def _get_lid(self, lid):
|
||
if lid in list(self.lid_dict.keys()):
|
||
return self.lid_dict[lid]
|
||
else:
|
||
raise ValueError(
|
||
f"The language {l} is not in {list(self.lid_dict.keys())}"
|
||
)
|
||
|
||
def _get_tnid(self, tnid):
|
||
if tnid in list(self.textnorm_dict.keys()):
|
||
return self.textnorm_dict[tnid]
|
||
else:
|
||
raise ValueError(
|
||
f"The textnorm {tnid} is not in {list(self.textnorm_dict.keys())}"
|
||
)
|
||
|
||
def read_tags(self, language_input, textnorm_input):
|
||
# handle language
|
||
if isinstance(language_input, list):
|
||
language_list = []
|
||
for l in language_input:
|
||
language_list.append(self._get_lid(l))
|
||
elif isinstance(language_input, str):
|
||
# if is existing file
|
||
if os.path.exists(language_input):
|
||
language_file = open(language_input, "r").readlines()
|
||
language_list = [
|
||
self._get_lid(l.strip())
|
||
for l in language_file
|
||
]
|
||
else:
|
||
language_list = [self._get_lid(language_input)]
|
||
else:
|
||
raise ValueError(
|
||
f"Unsupported type {type(language_input)} for language_input"
|
||
)
|
||
# handle textnorm
|
||
if isinstance(textnorm_input, list):
|
||
textnorm_list = []
|
||
for tn in textnorm_input:
|
||
textnorm_list.append(self._get_tnid(tn))
|
||
elif isinstance(textnorm_input, str):
|
||
# if is existing file
|
||
if os.path.exists(textnorm_input):
|
||
textnorm_file = open(textnorm_input, "r").readlines()
|
||
textnorm_list = [
|
||
self._get_tnid(tn.strip())
|
||
for tn in textnorm_file
|
||
]
|
||
else:
|
||
textnorm_list = [self._get_tnid(textnorm_input)]
|
||
else:
|
||
raise ValueError(
|
||
f"Unsupported type {type(textnorm_input)} for textnorm_input"
|
||
)
|
||
return language_list, textnorm_list
|
||
|
||
def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs):
|
||
language_input = kwargs.get("language", "auto")
|
||
textnorm_input = kwargs.get("textnorm", "woitn")
|
||
language_list, textnorm_list = self.read_tags(language_input, textnorm_input)
|
||
|
||
waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq)
|
||
waveform_nums = len(waveform_list)
|
||
|
||
assert len(language_list) == 1 or len(language_list) == waveform_nums, \
|
||
"length of parsed language list should be 1 or equal to the number of waveforms"
|
||
assert len(textnorm_list) == 1 or len(textnorm_list) == waveform_nums, \
|
||
"length of parsed textnorm list should be 1 or equal to the number of waveforms"
|
||
|
||
asr_res = []
|
||
for beg_idx in range(0, waveform_nums, self.batch_size):
|
||
end_idx = min(waveform_nums, beg_idx + self.batch_size)
|
||
feats, feats_len = self.extract_feat(waveform_list[beg_idx:end_idx])
|
||
_language_list = language_list[beg_idx:end_idx]
|
||
_textnorm_list = textnorm_list[beg_idx:end_idx]
|
||
if not len(_language_list):
|
||
_language_list = [language_list[0]]
|
||
_textnorm_list = [textnorm_list[0]]
|
||
B = feats.shape[0]
|
||
if len(_language_list) == 1 and B != 1:
|
||
_language_list = _language_list * B
|
||
if len(_textnorm_list) == 1 and B != 1:
|
||
_textnorm_list = _textnorm_list * B
|
||
ctc_logits, encoder_out_lens = self.infer(
|
||
feats,
|
||
feats_len,
|
||
np.array(_language_list, dtype=np.int32),
|
||
np.array(_textnorm_list, dtype=np.int32),
|
||
)
|
||
for b in range(feats.shape[0]):
|
||
# back to torch.Tensor
|
||
if isinstance(ctc_logits, np.ndarray):
|
||
ctc_logits = torch.from_numpy(ctc_logits).float()
|
||
# support batch_size=1 only currently
|
||
x = ctc_logits[b, : encoder_out_lens[b].item(), :]
|
||
yseq = x.argmax(dim=-1)
|
||
yseq = torch.unique_consecutive(yseq, dim=-1)
|
||
|
||
mask = yseq != self.blank_id
|
||
token_int = yseq[mask].tolist()
|
||
|
||
asr_res.append(self.tokenizer.decode(token_int))
|
||
|
||
return asr_res
|
||
|
||
def load_data(self, wav_content: Union[str, np.ndarray, List[str]], fs: int = None) -> List:
|
||
|
||
def convert_to_wav(input_path, output_path):
|
||
from pydub import AudioSegment
|
||
try:
|
||
audio = AudioSegment.from_mp3(input_path)
|
||
audio.export(output_path, format="wav")
|
||
print("音频文件为mp3格式，已转换为wav格式")
|
||
|
||
except Exception as e:
|
||
print(f"转换失败:{e}")
|
||
|
||
def load_wav(path: str) -> np.ndarray:
|
||
if not path.lower().endswith('.wav'):
|
||
import os
|
||
input_path = path
|
||
path = os.path.splitext(path)[0]+'.wav'
|
||
convert_to_wav(input_path,path) #将mp3格式转换成wav格式
|
||
|
||
waveform, _ = librosa.load(path, sr=fs)
|
||
return waveform
|
||
|
||
if isinstance(wav_content, np.ndarray):
|
||
return [wav_content]
|
||
|
||
if isinstance(wav_content, str):
|
||
return [load_wav(wav_content)]
|
||
|
||
if isinstance(wav_content, list):
|
||
return [load_wav(path) for path in wav_content]
|
||
|
||
raise TypeError(f"The type of {wav_content} is not in [str, np.ndarray, list]")
|
||
|
||
def extract_feat(self, waveform_list: List[np.ndarray]) -> Tuple[np.ndarray, np.ndarray]:
|
||
feats, feats_len = [], []
|
||
for waveform in waveform_list:
|
||
speech, _ = self.frontend.fbank(waveform)
|
||
|
||
if speech is None or speech.size == 0:
|
||
print("detected speech size {speech.size}")
|
||
raise ValueError("Empty speech detected, skipping this waveform.")
|
||
feat, feat_len = self.frontend.lfr_cmvn(speech)
|
||
feats.append(feat)
|
||
feats_len.append(feat_len)
|
||
|
||
feats = self.pad_feats(feats, np.max(feats_len))
|
||
feats_len = np.array(feats_len).astype(np.int32)
|
||
return feats, feats_len
|
||
|
||
@staticmethod
|
||
def pad_feats(feats: List[np.ndarray], max_feat_len: int) -> np.ndarray:
|
||
def pad_feat(feat: np.ndarray, cur_len: int) -> np.ndarray:
|
||
pad_width = ((0, max_feat_len - cur_len), (0, 0))
|
||
return np.pad(feat, pad_width, "constant", constant_values=0)
|
||
|
||
feat_res = [pad_feat(feat, feat.shape[0]) for feat in feats]
|
||
feats = np.array(feat_res).astype(np.float32)
|
||
return feats
|
||
|
||
def infer(
|
||
self,
|
||
feats: np.ndarray,
|
||
feats_len: np.ndarray,
|
||
language: np.ndarray,
|
||
textnorm: np.ndarray,
|
||
) -> Tuple[np.ndarray, np.ndarray]:
|
||
outputs = self.ort_infer([feats, feats_len, language, textnorm])
|
||
return outputs
|