From 94de39dde2e616a01683c518023d0fab72b4e103 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 19 二月 2024 22:21:50 +0800
Subject: [PATCH] aishell example
---
funasr/models/paraformer/model.py | 1036 ++++++++++++++++++++++++++++----------------------------
1 files changed, 516 insertions(+), 520 deletions(-)
diff --git a/funasr/models/paraformer/model.py b/funasr/models/paraformer/model.py
index 5492420..7747146 100644
--- a/funasr/models/paraformer/model.py
+++ b/funasr/models/paraformer/model.py
@@ -1,445 +1,440 @@
-import os
-import logging
-from typing import Union, Dict, List, Tuple, Optional
-
-import torch
-import torch.nn as nn
+#!/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)
import time
-
-from funasr.losses.label_smoothing_loss import (
- LabelSmoothingLoss, # noqa: H301
-)
-
-from funasr.models.paraformer.cif_predictor import mae_loss
-
-from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
-from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
-from funasr.metrics.compute_acc import th_accuracy
-from funasr.train_utils.device_funcs import force_gatherable
-
-from funasr.models.paraformer.search import Hypothesis
-
+import torch
+import logging
from torch.cuda.amp import autocast
+from typing import Union, Dict, List, Tuple, Optional
-from funasr.utils.load_utils import load_audio_and_text_image_video, extract_fbank, load_audio_and_text_image_video
-from funasr.utils import postprocess_utils
-from funasr.utils.datadir_writer import DatadirWriter
from funasr.register import tables
from funasr.models.ctc.ctc import CTC
+from funasr.utils import postprocess_utils
+from funasr.metrics.compute_acc import th_accuracy
+from funasr.utils.datadir_writer import DatadirWriter
+from funasr.models.paraformer.search import Hypothesis
+from funasr.models.paraformer.cif_predictor import mae_loss
+from funasr.train_utils.device_funcs import force_gatherable
+from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
+from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
+from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
+from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
+
@tables.register("model_classes", "Paraformer")
-class Paraformer(nn.Module):
- """
- 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,
- # token_list: Union[Tuple[str, ...], List[str]],
- 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,
- ctc: str = None,
- ctc_conf: Optional[Dict] = None,
- predictor: str = None,
- predictor_conf: Optional[Dict] = None,
- ctc_weight: float = 0.5,
- 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,
- # predictor=None,
- predictor_weight: float = 0.0,
- predictor_bias: int = 0,
- sampling_ratio: float = 0.2,
- share_embedding: bool = False,
- # preencoder: Optional[AbsPreEncoder] = None,
- # postencoder: Optional[AbsPostEncoder] = None,
- use_1st_decoder_loss: bool = False,
- **kwargs,
- ):
+class Paraformer(torch.nn.Module):
+ """
+ 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,
+ 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,
+ ctc: str = None,
+ ctc_conf: Optional[Dict] = None,
+ predictor: str = None,
+ predictor_conf: Optional[Dict] = None,
+ ctc_weight: float = 0.5,
+ 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,
+ # predictor=None,
+ predictor_weight: float = 0.0,
+ predictor_bias: int = 0,
+ sampling_ratio: float = 0.2,
+ share_embedding: bool = False,
+ # preencoder: Optional[AbsPreEncoder] = None,
+ # postencoder: Optional[AbsPostEncoder] = None,
+ use_1st_decoder_loss: bool = False,
+ **kwargs,
+ ):
- super().__init__()
+ super().__init__()
- if specaug is not None:
- specaug_class = tables.specaug_classes.get(specaug.lower())
- specaug = specaug_class(**specaug_conf)
- if normalize is not None:
- normalize_class = tables.normalize_classes.get(normalize.lower())
- normalize = normalize_class(**normalize_conf)
- encoder_class = tables.encoder_classes.get(encoder.lower())
- encoder = encoder_class(input_size=input_size, **encoder_conf)
- encoder_output_size = encoder.output_size()
+ 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()
- if decoder is not None:
- decoder_class = tables.decoder_classes.get(decoder.lower())
- decoder = decoder_class(
- vocab_size=vocab_size,
- encoder_output_size=encoder_output_size,
- **decoder_conf,
- )
- if ctc_weight > 0.0:
-
- if ctc_conf is None:
- ctc_conf = {}
-
- ctc = CTC(
- odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf
- )
- if predictor is not None:
- predictor_class = tables.predictor_classes.get(predictor.lower())
- predictor = predictor_class(**predictor_conf)
-
- # note that eos is the same as sos (equivalent ID)
- 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.ctc_weight = ctc_weight
- # self.token_list = token_list.copy()
- #
- # self.frontend = frontend
- self.specaug = specaug
- self.normalize = normalize
- # self.preencoder = preencoder
- # self.postencoder = postencoder
- self.encoder = encoder
- #
- # if not hasattr(self.encoder, "interctc_use_conditioning"):
- # self.encoder.interctc_use_conditioning = False
- # if self.encoder.interctc_use_conditioning:
- # self.encoder.conditioning_layer = torch.nn.Linear(
- # vocab_size, self.encoder.output_size()
- # )
- #
- # self.error_calculator = None
- #
- if ctc_weight == 1.0:
- self.decoder = None
- else:
- self.decoder = decoder
-
- self.criterion_att = LabelSmoothingLoss(
- size=vocab_size,
- padding_idx=ignore_id,
- smoothing=lsm_weight,
- normalize_length=length_normalized_loss,
- )
- #
- # if report_cer or report_wer:
- # self.error_calculator = ErrorCalculator(
- # token_list, sym_space, sym_blank, report_cer, report_wer
- # )
- #
- if ctc_weight == 0.0:
- self.ctc = None
- else:
- self.ctc = ctc
- #
- # self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
- self.predictor = predictor
- self.predictor_weight = predictor_weight
- self.predictor_bias = predictor_bias
- self.sampling_ratio = sampling_ratio
- self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
- # self.step_cur = 0
- #
- self.share_embedding = share_embedding
- if self.share_embedding:
- self.decoder.embed = None
-
- self.use_1st_decoder_loss = use_1st_decoder_loss
- self.length_normalized_loss = length_normalized_loss
- self.beam_search = None
-
- 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,)
- """
- # import pdb;
- # pdb.set_trace()
- 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]
-
-
- # Encoder
- encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
+ if decoder is not None:
+ decoder_class = tables.decoder_classes.get(decoder)
+ decoder = decoder_class(
+ vocab_size=vocab_size,
+ encoder_output_size=encoder_output_size,
+ **decoder_conf,
+ )
+ if ctc_weight > 0.0:
+
+ if ctc_conf is None:
+ ctc_conf = {}
+
+ ctc = CTC(
+ odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf
+ )
+ if predictor is not None:
+ predictor_class = tables.predictor_classes.get(predictor)
+ predictor = predictor_class(**predictor_conf)
+
+ # note that eos is the same as sos (equivalent ID)
+ 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.ctc_weight = ctc_weight
+ # self.token_list = token_list.copy()
+ #
+ # self.frontend = frontend
+ self.specaug = specaug
+ self.normalize = normalize
+ # self.preencoder = preencoder
+ # self.postencoder = postencoder
+ self.encoder = encoder
+ #
+ # if not hasattr(self.encoder, "interctc_use_conditioning"):
+ # self.encoder.interctc_use_conditioning = False
+ # if self.encoder.interctc_use_conditioning:
+ # self.encoder.conditioning_layer = torch.nn.Linear(
+ # vocab_size, self.encoder.output_size()
+ # )
+ #
+ # self.error_calculator = None
+ #
+ if ctc_weight == 1.0:
+ self.decoder = None
+ else:
+ self.decoder = decoder
+
+ self.criterion_att = LabelSmoothingLoss(
+ size=vocab_size,
+ padding_idx=ignore_id,
+ smoothing=lsm_weight,
+ normalize_length=length_normalized_loss,
+ )
+ #
+ # if report_cer or report_wer:
+ # self.error_calculator = ErrorCalculator(
+ # token_list, sym_space, sym_blank, report_cer, report_wer
+ # )
+ #
+ if ctc_weight == 0.0:
+ self.ctc = None
+ else:
+ self.ctc = ctc
+ #
+ # self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
+ self.predictor = predictor
+ self.predictor_weight = predictor_weight
+ self.predictor_bias = predictor_bias
+ self.sampling_ratio = sampling_ratio
+ self.criterion_pre = mae_loss(normalize_length=length_normalized_loss)
+ # self.step_cur = 0
+ #
+ self.share_embedding = share_embedding
+ if self.share_embedding:
+ self.decoder.embed = None
+
+ self.use_1st_decoder_loss = use_1st_decoder_loss
+ self.length_normalized_loss = length_normalized_loss
+ self.beam_search = None
+ self.error_calculator = None
+
+ 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,)
+ """
+ # import pdb;
+ # pdb.set_trace()
+ 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]
+
+
+ # Encoder
+ encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
-
- loss_ctc, cer_ctc = None, None
- loss_pre = None
- stats = dict()
-
- # decoder: CTC branch
- if self.ctc_weight != 0.0:
- loss_ctc, cer_ctc = self._calc_ctc_loss(
- encoder_out, encoder_out_lens, text, text_lengths
- )
-
- # Collect CTC branch stats
- stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
- stats["cer_ctc"] = cer_ctc
-
+
+ loss_ctc, cer_ctc = None, None
+ loss_pre = None
+ stats = dict()
+
+ # decoder: CTC branch
+ if self.ctc_weight != 0.0:
+ loss_ctc, cer_ctc = self._calc_ctc_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # Collect CTC branch stats
+ stats["loss_ctc"] = loss_ctc.detach() if loss_ctc is not None else None
+ stats["cer_ctc"] = cer_ctc
+
- # decoder: Attention decoder branch
- loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = self._calc_att_loss(
- encoder_out, encoder_out_lens, text, text_lengths
- )
-
- # 3. CTC-Att loss definition
- if self.ctc_weight == 0.0:
- loss = loss_att + loss_pre * self.predictor_weight
- else:
- loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
-
-
- # Collect Attn branch stats
- stats["loss_att"] = loss_att.detach() if loss_att is not None else None
- stats["pre_loss_att"] = pre_loss_att.detach() if pre_loss_att is not None else None
- stats["acc"] = acc_att
- stats["cer"] = cer_att
- stats["wer"] = wer_att
- stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
-
- stats["loss"] = torch.clone(loss.detach())
-
- # force_gatherable: to-device and to-tensor if scalar for DataParallel
- if self.length_normalized_loss:
- batch_size = (text_lengths + self.predictor_bias).sum()
- loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
- return loss, stats, weight
-
+ # decoder: Attention decoder branch
+ loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att = self._calc_att_loss(
+ encoder_out, encoder_out_lens, text, text_lengths
+ )
+
+ # 3. CTC-Att loss definition
+ if self.ctc_weight == 0.0:
+ loss = loss_att + loss_pre * self.predictor_weight
+ else:
+ loss = self.ctc_weight * loss_ctc + (1 - self.ctc_weight) * loss_att + loss_pre * self.predictor_weight
+
+
+ # Collect Attn branch stats
+ stats["loss_att"] = loss_att.detach() if loss_att is not None else None
+ stats["pre_loss_att"] = pre_loss_att.detach() if pre_loss_att is not None else None
+ stats["acc"] = acc_att
+ stats["cer"] = cer_att
+ stats["wer"] = wer_att
+ stats["loss_pre"] = loss_pre.detach().cpu() if loss_pre is not None else None
+
+ stats["loss"] = torch.clone(loss.detach())
+
+ # force_gatherable: to-device and to-tensor if scalar for DataParallel
+ if self.length_normalized_loss:
+ batch_size = (text_lengths + self.predictor_bias).sum()
+ 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]:
- """Encoder. Note that this method is used by asr_inference.py
- Args:
- speech: (Batch, Length, ...)
- speech_lengths: (Batch, )
- ind: int
- """
- with autocast(False):
+ def encode(
+ self, speech: torch.Tensor, speech_lengths: torch.Tensor, **kwargs,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """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)
-
+ # 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
- encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
- if isinstance(encoder_out, tuple):
- encoder_out = encoder_out[0]
+ # Forward encoder
+ encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
+ if isinstance(encoder_out, tuple):
+ encoder_out = encoder_out[0]
- return encoder_out, encoder_out_lens
-
- def calc_predictor(self, encoder_out, encoder_out_lens):
-
- encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
- encoder_out.device)
- pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(encoder_out, None,
- encoder_out_mask,
- ignore_id=self.ignore_id)
- return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
-
- def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
-
- decoder_outs = self.decoder(
- encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
- )
- decoder_out = decoder_outs[0]
- decoder_out = torch.log_softmax(decoder_out, dim=-1)
- return decoder_out, ys_pad_lens
+ return encoder_out, encoder_out_lens
+
+ def calc_predictor(self, encoder_out, encoder_out_lens):
+
+ encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
+ encoder_out.device)
+ pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(encoder_out, None,
+ encoder_out_mask,
+ ignore_id=self.ignore_id)
+ return pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index
+
+ def cal_decoder_with_predictor(self, encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens):
+
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
+ )
+ decoder_out = decoder_outs[0]
+ decoder_out = torch.log_softmax(decoder_out, dim=-1)
+ return decoder_out, ys_pad_lens
- def _calc_att_loss(
- self,
- encoder_out: torch.Tensor,
- encoder_out_lens: torch.Tensor,
- ys_pad: torch.Tensor,
- ys_pad_lens: torch.Tensor,
- ):
- encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
- encoder_out.device)
- if self.predictor_bias == 1:
- _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
- ys_pad_lens = ys_pad_lens + self.predictor_bias
- pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor(encoder_out, ys_pad, encoder_out_mask,
- ignore_id=self.ignore_id)
-
- # 0. sampler
- decoder_out_1st = None
- pre_loss_att = None
- if self.sampling_ratio > 0.0:
+ def _calc_att_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ):
+ encoder_out_mask = (~make_pad_mask(encoder_out_lens, maxlen=encoder_out.size(1))[:, None, :]).to(
+ encoder_out.device)
+ if self.predictor_bias == 1:
+ _, ys_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
+ ys_pad_lens = ys_pad_lens + self.predictor_bias
+ pre_acoustic_embeds, pre_token_length, _, pre_peak_index = self.predictor(encoder_out, ys_pad, encoder_out_mask,
+ ignore_id=self.ignore_id)
+
+ # 0. sampler
+ decoder_out_1st = None
+ pre_loss_att = None
+ if self.sampling_ratio > 0.0:
- sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
- pre_acoustic_embeds)
- else:
- sematic_embeds = pre_acoustic_embeds
-
- # 1. Forward decoder
- decoder_outs = self.decoder(
- encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
- )
- decoder_out, _ = decoder_outs[0], decoder_outs[1]
-
- if decoder_out_1st is None:
- decoder_out_1st = decoder_out
- # 2. Compute attention loss
- loss_att = self.criterion_att(decoder_out, ys_pad)
- acc_att = th_accuracy(
- decoder_out_1st.view(-1, self.vocab_size),
- ys_pad,
- ignore_label=self.ignore_id,
- )
- loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
-
- # Compute cer/wer using attention-decoder
- if self.training or self.error_calculator is None:
- cer_att, wer_att = None, None
- else:
- ys_hat = decoder_out_1st.argmax(dim=-1)
- cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
-
- return loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att
-
- def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):
-
- tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
- ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
- if self.share_embedding:
- ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
- else:
- ys_pad_embed = self.decoder.embed(ys_pad_masked)
- with torch.no_grad():
- decoder_outs = self.decoder(
- encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens
- )
- decoder_out, _ = decoder_outs[0], decoder_outs[1]
- pred_tokens = decoder_out.argmax(-1)
- nonpad_positions = ys_pad.ne(self.ignore_id)
- seq_lens = (nonpad_positions).sum(1)
- same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
- input_mask = torch.ones_like(nonpad_positions)
- bsz, seq_len = ys_pad.size()
- for li in range(bsz):
- target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
- if target_num > 0:
- input_mask[li].scatter_(dim=0,
- index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device),
- value=0)
- input_mask = input_mask.eq(1)
- input_mask = input_mask.masked_fill(~nonpad_positions, False)
- input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
-
- sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
- input_mask_expand_dim, 0)
- return sematic_embeds * tgt_mask, decoder_out * tgt_mask
-
- def _calc_ctc_loss(
- self,
- encoder_out: torch.Tensor,
- encoder_out_lens: torch.Tensor,
- ys_pad: torch.Tensor,
- ys_pad_lens: torch.Tensor,
- ):
- # Calc CTC loss
- loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
-
- # Calc CER using CTC
- cer_ctc = None
- if not self.training and self.error_calculator is not None:
- ys_hat = self.ctc.argmax(encoder_out).data
- cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
- return loss_ctc, cer_ctc
+ sematic_embeds, decoder_out_1st = self.sampler(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens,
+ pre_acoustic_embeds)
+ else:
+ sematic_embeds = pre_acoustic_embeds
+
+ # 1. Forward decoder
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, sematic_embeds, ys_pad_lens
+ )
+ decoder_out, _ = decoder_outs[0], decoder_outs[1]
+
+ if decoder_out_1st is None:
+ decoder_out_1st = decoder_out
+ # 2. Compute attention loss
+ loss_att = self.criterion_att(decoder_out, ys_pad)
+ acc_att = th_accuracy(
+ decoder_out_1st.view(-1, self.vocab_size),
+ ys_pad,
+ ignore_label=self.ignore_id,
+ )
+ loss_pre = self.criterion_pre(ys_pad_lens.type_as(pre_token_length), pre_token_length)
+
+ # Compute cer/wer using attention-decoder
+ if self.training or self.error_calculator is None:
+ cer_att, wer_att = None, None
+ else:
+ ys_hat = decoder_out_1st.argmax(dim=-1)
+ cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
+
+ return loss_att, acc_att, cer_att, wer_att, loss_pre, pre_loss_att
+
+ def sampler(self, encoder_out, encoder_out_lens, ys_pad, ys_pad_lens, pre_acoustic_embeds):
+
+ tgt_mask = (~make_pad_mask(ys_pad_lens, maxlen=ys_pad_lens.max())[:, :, None]).to(ys_pad.device)
+ ys_pad_masked = ys_pad * tgt_mask[:, :, 0]
+ if self.share_embedding:
+ ys_pad_embed = self.decoder.output_layer.weight[ys_pad_masked]
+ else:
+ ys_pad_embed = self.decoder.embed(ys_pad_masked)
+ with torch.no_grad():
+ decoder_outs = self.decoder(
+ encoder_out, encoder_out_lens, pre_acoustic_embeds, ys_pad_lens
+ )
+ decoder_out, _ = decoder_outs[0], decoder_outs[1]
+ pred_tokens = decoder_out.argmax(-1)
+ nonpad_positions = ys_pad.ne(self.ignore_id)
+ seq_lens = (nonpad_positions).sum(1)
+ same_num = ((pred_tokens == ys_pad) & nonpad_positions).sum(1)
+ input_mask = torch.ones_like(nonpad_positions)
+ bsz, seq_len = ys_pad.size()
+ for li in range(bsz):
+ target_num = (((seq_lens[li] - same_num[li].sum()).float()) * self.sampling_ratio).long()
+ if target_num > 0:
+ input_mask[li].scatter_(dim=0,
+ index=torch.randperm(seq_lens[li])[:target_num].to(input_mask.device),
+ value=0)
+ input_mask = input_mask.eq(1)
+ input_mask = input_mask.masked_fill(~nonpad_positions, False)
+ input_mask_expand_dim = input_mask.unsqueeze(2).to(pre_acoustic_embeds.device)
+
+ sematic_embeds = pre_acoustic_embeds.masked_fill(~input_mask_expand_dim, 0) + ys_pad_embed.masked_fill(
+ input_mask_expand_dim, 0)
+ return sematic_embeds * tgt_mask, decoder_out * tgt_mask
+
+ def _calc_ctc_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ ):
+ # Calc CTC loss
+ loss_ctc = self.ctc(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
+
+ # Calc CER using CTC
+ cer_ctc = None
+ if not self.training and self.error_calculator is not None:
+ ys_hat = self.ctc.argmax(encoder_out).data
+ cer_ctc = self.error_calculator(ys_hat.cpu(), ys_pad.cpu(), is_ctc=True)
+ return loss_ctc, cer_ctc
-
- def init_beam_search(self,
- **kwargs,
- ):
- from funasr.models.paraformer.search import BeamSearchPara
- from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
- from funasr.models.transformer.scorers.length_bonus import LengthBonus
-
- # 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)),
- )
+
+ def init_beam_search(self,
+ **kwargs,
+ ):
+ from funasr.models.paraformer.search import BeamSearchPara
+ from funasr.models.transformer.scorers.ctc import CTCPrefixScorer
+ from funasr.models.transformer.scorers.length_bonus import LengthBonus
+
+ # 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
-
- weights = dict(
- decoder=1.0 - kwargs.get("decoding_ctc_weight"),
- ctc=kwargs.get("decoding_ctc_weight", 0.0),
- lm=kwargs.get("lm_weight", 0.0),
- ngram=kwargs.get("ngram_weight", 0.0),
- length_bonus=kwargs.get("penalty", 0.0),
- )
- beam_search = BeamSearchPara(
- beam_size=kwargs.get("beam_size", 2),
- weights=weights,
- scorers=scorers,
- sos=self.sos,
- eos=self.eos,
- vocab_size=len(token_list),
- token_list=token_list,
- pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
- )
- # 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 generate(self,
+
+ # 3. Build ngram model
+ # ngram is not supported now
+ ngram = None
+ scorers["ngram"] = ngram
+
+ weights = dict(
+ decoder=1.0 - kwargs.get("decoding_ctc_weight"),
+ ctc=kwargs.get("decoding_ctc_weight", 0.0),
+ lm=kwargs.get("lm_weight", 0.0),
+ ngram=kwargs.get("ngram_weight", 0.0),
+ length_bonus=kwargs.get("penalty", 0.0),
+ )
+ beam_search = BeamSearchPara(
+ beam_size=kwargs.get("beam_size", 2),
+ weights=weights,
+ scorers=scorers,
+ sos=self.sos,
+ eos=self.eos,
+ vocab_size=len(token_list),
+ token_list=token_list,
+ pre_beam_score_key=None if self.ctc_weight == 1.0 else "full",
+ )
+ # 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,
data_lengths=None,
key: list=None,
@@ -447,107 +442,108 @@
frontend=None,
**kwargs,
):
-
- # 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 = {}
- if isinstance(data_in, torch.Tensor): # fbank
- speech, speech_lengths = data_in, data_lengths
- if len(speech.shape) < 3:
- speech = speech[None, :, :]
- if speech_lengths is None:
- speech_lengths = speech.shape[1]
- else:
- # extract fbank feats
- time1 = time.perf_counter()
- audio_sample_list = load_audio_and_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000), data_type=kwargs.get("data_type", "sound"), tokenizer=tokenizer)
- 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=frontend)
- time3 = time.perf_counter()
- meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
- meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
-
- speech.to(device=kwargs["device"]), 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]
-
- # predictor
- predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
- pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
- predictor_outs[2], predictor_outs[3]
- pre_token_length = pre_token_length.round().long()
- if torch.max(pre_token_length) < 1:
- return []
- decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens, pre_acoustic_embeds,
- pre_token_length)
- decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
+ # 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 = {}
+ if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # fbank
+ speech, speech_lengths = data_in, data_lengths
+ if len(speech.shape) < 3:
+ speech = speech[None, :, :]
+ if speech_lengths is None:
+ speech_lengths = speech.shape[1]
+ else:
+ # extract fbank feats
+ time1 = time.perf_counter()
+ audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000), data_type=kwargs.get("data_type", "sound"), tokenizer=tokenizer)
+ 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=frontend)
+ time3 = time.perf_counter()
+ meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * 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]
+
+ # predictor
+ predictor_outs = self.calc_predictor(encoder_out, encoder_out_lens)
+ pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
+ predictor_outs[2], predictor_outs[3]
+ pre_token_length = pre_token_length.round().long()
+ if torch.max(pre_token_length) < 1:
+ return []
+ decoder_outs = self.cal_decoder_with_predictor(encoder_out, encoder_out_lens, pre_acoustic_embeds,
+ pre_token_length)
+ decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
- results = []
- b, n, d = decoder_out.size()
- if isinstance(key[0], (list, tuple)):
- key = key[0]
- for i in range(b):
- x = encoder_out[i, :encoder_out_lens[i], :]
- am_scores = decoder_out[i, :pre_token_length[i], :]
- if self.beam_search is not None:
- nbest_hyps = self.beam_search(
- x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)
- )
-
- nbest_hyps = nbest_hyps[: self.nbest]
- else:
+ results = []
+ b, n, d = decoder_out.size()
+ if isinstance(key[0], (list, tuple)):
+ key = key[0]
+ if len(key) < b:
+ key = key*b
+ for i in range(b):
+ x = encoder_out[i, :encoder_out_lens[i], :]
+ am_scores = decoder_out[i, :pre_token_length[i], :]
+ if self.beam_search is not None:
+ nbest_hyps = self.beam_search(
+ x=x, am_scores=am_scores, maxlenratio=kwargs.get("maxlenratio", 0.0), minlenratio=kwargs.get("minlenratio", 0.0)
+ )
+
+ nbest_hyps = nbest_hyps[: self.nbest]
+ else:
- yseq = am_scores.argmax(dim=-1)
- score = am_scores.max(dim=-1)[0]
- score = torch.sum(score, dim=-1)
- # pad with mask tokens to ensure compatibility with sos/eos tokens
- yseq = torch.tensor(
- [self.sos] + yseq.tolist() + [self.eos], device=yseq.device
- )
- nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
- for nbest_idx, hyp in enumerate(nbest_hyps):
- ibest_writer = None
- if ibest_writer is None and kwargs.get("output_dir") is not None:
- writer = DatadirWriter(kwargs.get("output_dir"))
- ibest_writer = 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))
-
- if tokenizer is not None:
- # 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], "text": text_postprocessed}
+ yseq = am_scores.argmax(dim=-1)
+ score = am_scores.max(dim=-1)[0]
+ score = torch.sum(score, dim=-1)
+ # pad with mask tokens to ensure compatibility with sos/eos tokens
+ yseq = torch.tensor(
+ [self.sos] + yseq.tolist() + [self.eos], device=yseq.device
+ )
+ nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
+ for nbest_idx, hyp in enumerate(nbest_hyps):
+ ibest_writer = None
+ if ibest_writer is None and kwargs.get("output_dir") is not None:
+ writer = DatadirWriter(kwargs.get("output_dir"))
+ ibest_writer = 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))
+
+ if tokenizer is not None:
+ # Change integer-ids to tokens
+ token = tokenizer.ids2tokens(token_int)
+ text_postprocessed = tokenizer.tokens2text(token)
+ if not hasattr(tokenizer, "bpemodel"):
+ text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
+
+ result_i = {"key": key[i], "text": text_postprocessed}
-
- if ibest_writer is not None:
- ibest_writer["token"][key[i]] = " ".join(token)
- # ibest_writer["text"][key[i]] = text
- ibest_writer["text"][key[i]] = text_postprocessed
- else:
- result_i = {"key": key[i], "token_int": token_int}
- results.append(result_i)
-
- return results, meta_data
+
+ if ibest_writer is not None:
+ ibest_writer["token"][key[i]] = " ".join(token)
+ # ibest_writer["text"][key[i]] = text
+ ibest_writer["text"][key[i]] = text_postprocessed
+ else:
+ result_i = {"key": key[i], "token_int": token_int}
+ results.append(result_i)
+
+ return results, meta_data
--
Gitblit v1.9.1