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Zero
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# Copyright (c) 2023 Binbin Zhang ([email protected])
#
# 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
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