speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

update transducer demo

Browse Source
This commit is contained in:
shixian.shi 2024-02-21 19:50:18 +08:00
parent cdca62d933
commit ec4ca408bf
2 changed files with 18 additions and 485 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

15
examples/industrial_data_pretraining/transducer/demo.py Normal file
View File

@ -0,0 +1,15 @@
#!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
from funasr import AutoModel
# Transducer, BAT and RWKV_BAT models are just same to use, use the correct model_revision
# https://modelscope.cn/models?name=transducer&page=1&tasks=auto-speech-recognition&type=audio
model = AutoModel(model="iic/speech_bat_asr-zh-cn-16k-aishell1-vocab4234-pytorch",
model_revision="v2.0.2",
)
res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
print(res)

488
funasr/models/bat/model.py
View File

@ -13,6 +13,7 @@ from distutils.version import LooseVersion
from funasr.register import tables
from funasr.utils import postprocess_utils
from funasr.utils.datadir_writer import DatadirWriter
from funasr.models.transducer.model import Transducer
from funasr.train_utils.device_funcs import force_gatherable
from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
@ -32,488 +33,5 @@ else:
@tables.register("model_classes", "BAT") # TODO: BAT training
class BAT(torch.nn.Module):
def __init__(
self,
frontend: Optional[str] = None,
frontend_conf: Optional[Dict] = None,
specaug: Optional[str] = None,
specaug_conf: Optional[Dict] = None,
normalize: str = None,
normalize_conf: Optional[Dict] = None,
encoder: str = None,
encoder_conf: Optional[Dict] = None,
decoder: str = None,
decoder_conf: Optional[Dict] = None,
joint_network: str = None,
joint_network_conf: Optional[Dict] = None,
transducer_weight: float = 1.0,
fastemit_lambda: float = 0.0,
auxiliary_ctc_weight: float = 0.0,
auxiliary_ctc_dropout_rate: float = 0.0,
auxiliary_lm_loss_weight: float = 0.0,
auxiliary_lm_loss_smoothing: float = 0.0,
input_size: int = 80,
vocab_size: int = -1,
ignore_id: int = -1,
blank_id: int = 0,
sos: int = 1,
eos: int = 2,
lsm_weight: float = 0.0,
length_normalized_loss: bool = False,
# report_cer: bool = True,
# report_wer: bool = True,
# sym_space: str = "<space>",
# sym_blank: str = "<blank>",
# extract_feats_in_collect_stats: bool = True,
share_embedding: bool = False,
# preencoder: Optional[AbsPreEncoder] = None,
# postencoder: Optional[AbsPostEncoder] = None,
**kwargs,
):
super().__init__()
if specaug is not None:
specaug_class = tables.specaug_classes.get(specaug)
specaug = specaug_class(**specaug_conf)
if normalize is not None:
normalize_class = tables.normalize_classes.get(normalize)
normalize = normalize_class(**normalize_conf)
encoder_class = tables.encoder_classes.get(encoder)
encoder = encoder_class(input_size=input_size, **encoder_conf)
encoder_output_size = encoder.output_size()
decoder_class = tables.decoder_classes.get(decoder)
decoder = decoder_class(
vocab_size=vocab_size,
**decoder_conf,
)
decoder_output_size = decoder.output_size
joint_network_class = tables.joint_network_classes.get(joint_network)
joint_network = joint_network_class(
vocab_size,
encoder_output_size,
decoder_output_size,
**joint_network_conf,
)
self.criterion_transducer = None
self.error_calculator = None
self.use_auxiliary_ctc = auxiliary_ctc_weight > 0
self.use_auxiliary_lm_loss = auxiliary_lm_loss_weight > 0
if self.use_auxiliary_ctc:
self.ctc_lin = torch.nn.Linear(encoder.output_size(), vocab_size)
self.ctc_dropout_rate = auxiliary_ctc_dropout_rate
if self.use_auxiliary_lm_loss:
self.lm_lin = torch.nn.Linear(decoder.output_size, vocab_size)
self.lm_loss_smoothing = auxiliary_lm_loss_smoothing
self.transducer_weight = transducer_weight
self.fastemit_lambda = fastemit_lambda
self.auxiliary_ctc_weight = auxiliary_ctc_weight
self.auxiliary_lm_loss_weight = auxiliary_lm_loss_weight
self.blank_id = blank_id
self.sos = sos if sos is not None else vocab_size - 1
self.eos = eos if eos is not None else vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.encoder = encoder
self.decoder = decoder
self.joint_network = joint_network
self.criterion_att = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
smoothing=lsm_weight,
normalize_length=length_normalized_loss,
)
self.length_normalized_loss = length_normalized_loss
self.beam_search = None
self.ctc = None
self.ctc_weight = 0.0
def forward(
self,
speech: torch.Tensor,
speech_lengths: torch.Tensor,
text: torch.Tensor,
text_lengths: torch.Tensor,
**kwargs,
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
"""Encoder + Decoder + Calc loss
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
text: (Batch, Length)
text_lengths: (Batch,)
"""
if len(text_lengths.size()) > 1:
text_lengths = text_lengths[:, 0]
if len(speech_lengths.size()) > 1:
speech_lengths = speech_lengths[:, 0]
batch_size = speech.shape[0]
# 1. Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
if hasattr(self.encoder, 'overlap_chunk_cls') and self.encoder.overlap_chunk_cls is not None:
encoder_out, encoder_out_lens = self.encoder.overlap_chunk_cls.remove_chunk(encoder_out, encoder_out_lens,
chunk_outs=None)
# 2. Transducer-related I/O preparation
decoder_in, target, t_len, u_len = get_transducer_task_io(
text,
encoder_out_lens,
ignore_id=self.ignore_id,
)
# 3. Decoder
self.decoder.set_device(encoder_out.device)
decoder_out = self.decoder(decoder_in, u_len)
# 4. Joint Network
joint_out = self.joint_network(
encoder_out.unsqueeze(2), decoder_out.unsqueeze(1)
)
# 5. Losses
loss_trans, cer_trans, wer_trans = self._calc_transducer_loss(
encoder_out,
joint_out,
target,
t_len,
u_len,
)
loss_ctc, loss_lm = 0.0, 0.0
if self.use_auxiliary_ctc:
loss_ctc = self._calc_ctc_loss(
encoder_out,
target,
t_len,
u_len,
)
if self.use_auxiliary_lm_loss:
loss_lm = self._calc_lm_loss(decoder_out, target)
loss = (
self.transducer_weight * loss_trans
+ self.auxiliary_ctc_weight * loss_ctc
+ self.auxiliary_lm_loss_weight * loss_lm
)
stats = dict(
loss=loss.detach(),
loss_transducer=loss_trans.detach(),
aux_ctc_loss=loss_ctc.detach() if loss_ctc > 0.0 else None,
aux_lm_loss=loss_lm.detach() if loss_lm > 0.0 else None,
cer_transducer=cer_trans,
wer_transducer=wer_trans,
)
# force_gatherable: to-device and to-tensor if scalar for DataParallel
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
return loss, stats, weight
def encode(
self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Frontend + Encoder. Note that this method is used by asr_inference.py
Args:
speech: (Batch, Length, ...)
speech_lengths: (Batch, )
ind: int
"""
with autocast(False):
# Data augmentation
if self.specaug is not None and self.training:
speech, speech_lengths = self.specaug(speech, speech_lengths)
# Normalization for feature: e.g. Global-CMVN, Utterance-CMVN
if self.normalize is not None:
speech, speech_lengths = self.normalize(speech, speech_lengths)
# Forward encoder
# feats: (Batch, Length, Dim)
# -> encoder_out: (Batch, Length2, Dim2)
encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
intermediate_outs = None
if isinstance(encoder_out, tuple):
intermediate_outs = encoder_out[1]
encoder_out = encoder_out[0]
if intermediate_outs is not None:
return (encoder_out, intermediate_outs), encoder_out_lens
return encoder_out, encoder_out_lens
def _calc_transducer_loss(
self,
encoder_out: torch.Tensor,
joint_out: torch.Tensor,
target: torch.Tensor,
t_len: torch.Tensor,
u_len: torch.Tensor,
) -> Tuple[torch.Tensor, Optional[float], Optional[float]]:
"""Compute Transducer loss.
Args:
encoder_out: Encoder output sequences. (B, T, D_enc)
joint_out: Joint Network output sequences (B, T, U, D_joint)
target: Target label ID sequences. (B, L)
t_len: Encoder output sequences lengths. (B,)
u_len: Target label ID sequences lengths. (B,)
Return:
loss_transducer: Transducer loss value.
cer_transducer: Character error rate for Transducer.
wer_transducer: Word Error Rate for Transducer.
"""
if self.criterion_transducer is None:
try:
from warp_rnnt import rnnt_loss as RNNTLoss
self.criterion_transducer = RNNTLoss
except ImportError:
logging.error(
"warp-rnnt was not installed."
"Please consult the installation documentation."
)
exit(1)
log_probs = torch.log_softmax(joint_out, dim=-1)
loss_transducer = self.criterion_transducer(
log_probs,
target,
t_len,
u_len,
reduction="mean",
blank=self.blank_id,
fastemit_lambda=self.fastemit_lambda,
gather=True,
)
if not self.training and (self.report_cer or self.report_wer):
if self.error_calculator is None:
from funasr.metrics import ErrorCalculatorTransducer as ErrorCalculator
self.error_calculator = ErrorCalculator(
self.decoder,
self.joint_network,
self.token_list,
self.sym_space,
self.sym_blank,
report_cer=self.report_cer,
report_wer=self.report_wer,
)
cer_transducer, wer_transducer = self.error_calculator(encoder_out, target, t_len)
return loss_transducer, cer_transducer, wer_transducer
return loss_transducer, None, None
def _calc_ctc_loss(
self,
encoder_out: torch.Tensor,
target: torch.Tensor,
t_len: torch.Tensor,
u_len: torch.Tensor,
) -> torch.Tensor:
"""Compute CTC loss.
Args:
encoder_out: Encoder output sequences. (B, T, D_enc)
target: Target label ID sequences. (B, L)
t_len: Encoder output sequences lengths. (B,)
u_len: Target label ID sequences lengths. (B,)
Return:
loss_ctc: CTC loss value.
"""
ctc_in = self.ctc_lin(
torch.nn.functional.dropout(encoder_out, p=self.ctc_dropout_rate)
)
ctc_in = torch.log_softmax(ctc_in.transpose(0, 1), dim=-1)
target_mask = target != 0
ctc_target = target[target_mask].cpu()
with torch.backends.cudnn.flags(deterministic=True):
loss_ctc = torch.nn.functional.ctc_loss(
ctc_in,
ctc_target,
t_len,
u_len,
zero_infinity=True,
reduction="sum",
)
loss_ctc /= target.size(0)
return loss_ctc
def _calc_lm_loss(
self,
decoder_out: torch.Tensor,
target: torch.Tensor,
) -> torch.Tensor:
"""Compute LM loss.
Args:
decoder_out: Decoder output sequences. (B, U, D_dec)
target: Target label ID sequences. (B, L)
Return:
loss_lm: LM loss value.
"""
lm_loss_in = self.lm_lin(decoder_out[:, :-1, :]).view(-1, self.vocab_size)
lm_target = target.view(-1).type(torch.int64)
with torch.no_grad():
true_dist = lm_loss_in.clone()
true_dist.fill_(self.lm_loss_smoothing / (self.vocab_size - 1))
# Ignore blank ID (0)
ignore = lm_target == 0
lm_target = lm_target.masked_fill(ignore, 0)
true_dist.scatter_(1, lm_target.unsqueeze(1), (1 - self.lm_loss_smoothing))
loss_lm = torch.nn.functional.kl_div(
torch.log_softmax(lm_loss_in, dim=1),
true_dist,
reduction="none",
)
loss_lm = loss_lm.masked_fill(ignore.unsqueeze(1), 0).sum() / decoder_out.size(
0
)
return loss_lm
def init_beam_search(self,
**kwargs,
):
# 1. Build ASR model
scorers = {}
if self.ctc != None:
ctc = CTCPrefixScorer(ctc=self.ctc, eos=self.eos)
scorers.update(
ctc=ctc
)
token_list = kwargs.get("token_list")
scorers.update(
length_bonus=LengthBonus(len(token_list)),
)
# 3. Build ngram model
# ngram is not supported now
ngram = None
scorers["ngram"] = ngram
beam_search = BeamSearchTransducer(
self.decoder,
self.joint_network,
kwargs.get("beam_size", 2),
nbest=1,
)
# beam_search.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
# for scorer in scorers.values():
# if isinstance(scorer, torch.nn.Module):
# scorer.to(device=kwargs.get("device", "cpu"), dtype=getattr(torch, kwargs.get("dtype", "float32"))).eval()
self.beam_search = beam_search
def inference(self,
data_in: list,
data_lengths: list=None,
key: list=None,
tokenizer=None,
**kwargs,
):
if kwargs.get("batch_size", 1) > 1:
raise NotImplementedError("batch decoding is not implemented")
# init beamsearch
is_use_ctc = kwargs.get("decoding_ctc_weight", 0.0) > 0.00001 and self.ctc != None
is_use_lm = kwargs.get("lm_weight", 0.0) > 0.00001 and kwargs.get("lm_file", None) is not None
# if self.beam_search is None and (is_use_lm or is_use_ctc):
logging.info("enable beam_search")
self.init_beam_search(**kwargs)
self.nbest = kwargs.get("nbest", 1)
meta_data = {}
# extract fbank feats
time1 = time.perf_counter()
audio_sample_list = load_audio_text_image_video(data_in, fs=self.frontend.fs, audio_fs=kwargs.get("fs", 16000))
time2 = time.perf_counter()
meta_data["load_data"] = f"{time2 - time1:0.3f}"
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=self.frontend)
time3 = time.perf_counter()
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
meta_data["batch_data_time"] = speech_lengths.sum().item() * self.frontend.frame_shift * self.frontend.lfr_n / 1000
speech = speech.to(device=kwargs["device"])
speech_lengths = speech_lengths.to(device=kwargs["device"])
# Encoder
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
if isinstance(encoder_out, tuple):
encoder_out = encoder_out[0]
# c. Passed the encoder result and the beam search
nbest_hyps = self.beam_search(encoder_out[0], is_final=True)
nbest_hyps = nbest_hyps[: self.nbest]
results = []
b, n, d = encoder_out.size()
for i in range(b):
for nbest_idx, hyp in enumerate(nbest_hyps):
ibest_writer = None
if kwargs.get("output_dir") is not None:
if not hasattr(self, "writer"):
self.writer = DatadirWriter(kwargs.get("output_dir"))
ibest_writer = self.writer[f"{nbest_idx + 1}best_recog"]
# remove sos/eos and get results
last_pos = -1
if isinstance(hyp.yseq, list):
token_int = hyp.yseq#[1:last_pos]
else:
token_int = hyp.yseq#[1:last_pos].tolist()
# remove blank symbol id, which is assumed to be 0
token_int = list(filter(lambda x: x != self.eos and x != self.sos and x != self.blank_id, token_int))
# Change integer-ids to tokens
token = tokenizer.ids2tokens(token_int)
text = tokenizer.tokens2text(token)
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
result_i = {"key": key[i], "token": token, "text": text, "text_postprocessed": text_postprocessed}
results.append(result_i)
if ibest_writer is not None:
ibest_writer["token"][key[i]] = " ".join(token)
ibest_writer["text"][key[i]] = text
ibest_writer["text_postprocessed"][key[i]] = text_postprocessed
return results, meta_data
class BAT(Transducer):
pass