Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,183 Bytes
568e264 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 |
# Copyright (c) 2023 Binbin Zhang (binbzha@qq.com)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import torchaudio
import torchaudio.compliance.kaldi as kaldi
from wenet.cli.hub import Hub
from wenet.utils.ctc_utils import (force_align, gen_ctc_peak_time,
gen_timestamps_from_peak)
from wenet.utils.file_utils import read_symbol_table
from wenet.transformer.search import (attention_rescoring,
ctc_prefix_beam_search, DecodeResult)
from wenet.utils.context_graph import ContextGraph
from wenet.utils.common import TORCH_NPU_AVAILABLE # noqa just ensure to check torch-npu
class Model:
def __init__(self,
model_dir: str,
gpu: int = -1,
beam: int = 5,
context_path: str = None,
context_score: float = 6.0,
resample_rate: int = 16000):
model_path = os.path.join(model_dir, 'final.zip')
units_path = os.path.join(model_dir, 'units.txt')
self.model = torch.jit.load(model_path)
self.resample_rate = resample_rate
self.model.eval()
if gpu >= 0:
device = 'cuda:{}'.format(gpu)
else:
device = 'cpu'
self.device = torch.device(device)
self.model.to(device)
self.symbol_table = read_symbol_table(units_path)
self.char_dict = {v: k for k, v in self.symbol_table.items()}
self.beam = beam
if context_path is not None:
self.context_graph = ContextGraph(context_path,
self.symbol_table,
context_score=context_score)
else:
self.context_graph = None
def compute_feats(self, audio_file: str) -> torch.Tensor:
waveform, sample_rate = torchaudio.load(audio_file, normalize=False)
waveform = waveform.to(torch.float)
if sample_rate != self.resample_rate:
waveform = torchaudio.transforms.Resample(
orig_freq=sample_rate, new_freq=self.resample_rate)(waveform)
# NOTE (MengqingCao): complex dtype not supported in torch_npu.abs() now,
# thus, delay placing data on NPU after the calculation of fbank.
# revert me after complex dtype is supported.
if "npu" not in self.device.__str__():
waveform = waveform.to(self.device)
feats = kaldi.fbank(waveform,
num_mel_bins=80,
frame_length=25,
frame_shift=10,
energy_floor=0.0,
sample_frequency=self.resample_rate)
if "npu" in self.device.__str__():
feats = feats.to(self.device)
feats = feats.unsqueeze(0)
return feats
@torch.no_grad()
def _decode(self,
audio_file: str,
tokens_info: bool = False,
label: str = None) -> dict:
feats = self.compute_feats(audio_file)
encoder_out, _, _ = self.model.forward_encoder_chunk(feats, 0, -1)
encoder_lens = torch.tensor([encoder_out.size(1)],
dtype=torch.long,
device=encoder_out.device)
ctc_probs = self.model.ctc_activation(encoder_out)
if label is None:
ctc_prefix_results = ctc_prefix_beam_search(
ctc_probs,
encoder_lens,
self.beam,
context_graph=self.context_graph)
else: # force align mode, construct ctc prefix result from alignment
label_t = self.tokenize(label)
alignment = force_align(ctc_probs.squeeze(0),
torch.tensor(label_t, dtype=torch.long))
peaks = gen_ctc_peak_time(alignment)
ctc_prefix_results = [
DecodeResult(tokens=label_t,
score=0.0,
times=peaks,
nbest=[label_t],
nbest_scores=[0.0],
nbest_times=[peaks])
]
rescoring_results = attention_rescoring(self.model, ctc_prefix_results,
encoder_out, encoder_lens, 0.3,
0.5)
res = rescoring_results[0]
result = {}
result['text'] = ''.join([self.char_dict[x] for x in res.tokens])
result['confidence'] = res.confidence
if tokens_info:
frame_rate = self.model.subsampling_rate(
) * 0.01 # 0.01 seconds per frame
max_duration = encoder_out.size(1) * frame_rate
times = gen_timestamps_from_peak(res.times, max_duration,
frame_rate, 1.0)
tokens_info = []
for i, x in enumerate(res.tokens):
tokens_info.append({
'token': self.char_dict[x],
'start': round(times[i][0], 3),
'end': round(times[i][1], 3),
'confidence': round(res.tokens_confidence[i], 2)
})
result['tokens'] = tokens_info
return result
def transcribe(self, audio_file: str, tokens_info: bool = False) -> dict:
return self._decode(audio_file, tokens_info)
def tokenize(self, label: str):
# TODO(Binbin Zhang): Support BPE
tokens = []
for c in label:
if c == ' ':
c = "▁"
tokens.append(c)
token_list = []
for c in tokens:
if c in self.symbol_table:
token_list.append(self.symbol_table[c])
elif '<unk>' in self.symbol_table:
token_list.append(self.symbol_table['<unk>'])
return token_list
def align(self, audio_file: str, label: str) -> dict:
return self._decode(audio_file, True, label)
def load_model(language: str = None,
model_dir: str = None,
gpu: int = -1,
beam: int = 5,
context_path: str = None,
context_score: float = 6.0,
device: str = "cpu") -> Model:
if model_dir is None:
model_dir = Hub.get_model_by_lang(language)
if gpu != -1:
# remain the original usage of gpu
device = "cuda"
model = Model(model_dir, gpu, beam, context_path, context_score)
model.device = torch.device(device)
model.model.to(device)
return model
|