From abb33d6b2097e5b0643326bc1b376a63cdc2f967 Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期一, 24 六月 2024 17:06:21 +0800
Subject: [PATCH] Dev gzf deepspeed (#1844)
---
funasr/models/sense_voice/model.py | 282 +-------------------------------------------------------
1 files changed, 6 insertions(+), 276 deletions(-)
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index a9b2149..9db6539 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -10,7 +10,7 @@
from torch import Tensor
from torch import nn
from torch.cuda.amp import autocast
-from funasr.metrics.compute_acc import compute_accuracy
+from funasr.metrics.compute_acc import compute_accuracy, th_accuracy
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
from funasr.train_utils.device_funcs import force_gatherable
from . import whisper_lib as whisper
@@ -662,9 +662,11 @@
else:
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
- loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
- encoder_out, encoder_out_lens, text, text_lengths, target_mask=target_mask
- )
+ with autocast(False):
+ loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
+ encoder_out, encoder_out_lens, text, text_lengths, target_mask=target_mask
+ )
+
loss = loss_att
stats = {}
stats["acc"] = acc_att
@@ -1390,275 +1392,3 @@
from funasr.models.paraformer.search import Hypothesis
from funasr.utils import postprocess_utils
-
-
-@tables.register("model_classes", "SenseVoiceSANMCTC")
-class SenseVoiceSANMCTC(nn.Module):
- """CTC-attention hybrid Encoder-Decoder model"""
-
- def __init__(
- self,
- specaug: str = None,
- specaug_conf: dict = None,
- normalize: str = None,
- normalize_conf: dict = None,
- encoder: str = None,
- encoder_conf: dict = None,
- ctc_conf: dict = None,
- input_size: int = 80,
- vocab_size: int = -1,
- ignore_id: int = -1,
- blank_id: int = 0,
- sos: int = 1,
- eos: int = 2,
- length_normalized_loss: bool = False,
- **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()
-
- if ctc_conf is None:
- ctc_conf = {}
- ctc = CTC(odim=vocab_size, encoder_output_size=encoder_output_size, **ctc_conf)
-
- 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.specaug = specaug
- self.normalize = normalize
- self.encoder = encoder
- self.error_calculator = None
-
- self.ctc = ctc
-
- self.length_normalized_loss = length_normalized_loss
- self.encoder_output_size = encoder_output_size
-
- self.lid_dict = {"zh": 3, "en": 4, "yue": 7, "ja": 11, "ko": 12, "nospeech": 13}
- self.textnorm_dict = {"withtextnorm": 14, "wotextnorm": 15}
- self.embed = torch.nn.Embedding(8 + len(self.lid_dict) + len(self.textnorm_dict), 560)
-
- def forward(
- self,
- speech: torch.Tensor,
- speech_lengths: torch.Tensor,
- text: torch.Tensor,
- text_lengths: torch.Tensor,
- **kwargs,
- ):
- """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]
-
- # 1. Encoder
- encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
-
- loss_ctc, cer_ctc = None, None
- stats = dict()
-
- loss_ctc, cer_ctc = self._calc_ctc_loss(encoder_out, encoder_out_lens, text, text_lengths)
-
- loss = loss_ctc
-
- # Collect total loss stats
- stats["loss"] = torch.clone(loss.detach())
-
- # force_gatherable: to-device and to-tensor if scalar for DataParallel
- if self.length_normalized_loss:
- batch_size = int((text_lengths + 1).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,
- ):
- """Frontend + Encoder. Note that this method is used by asr_inference.py
- Args:
- speech: (Batch, Length, ...)
- speech_lengths: (Batch, )
- ind: int
- """
-
- # 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)
-
- return encoder_out, encoder_out_lens
-
- 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 inference(
- self,
- data_in,
- data_lengths=None,
- key: list = None,
- tokenizer=None,
- frontend=None,
- **kwargs,
- ):
-
- if kwargs.get("batch_size", 1) > 1:
- raise NotImplementedError("batch decoding is not implemented")
-
- 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"])
-
- language = kwargs.get("language", None)
- if language is not None:
- language_query = self.embed(
- torch.LongTensor(
- [[self.lid_dict[language] if language in self.lid_dict else 0]]
- ).to(speech.device)
- ).repeat(speech.size(0), 1, 1)
- else:
- language_query = self.embed(torch.LongTensor([[0]]).to(speech.device)).repeat(
- speech.size(0), 1, 1
- )
- textnorm = kwargs.get("text_norm", "wotextnorm")
- textnorm_query = self.embed(
- torch.LongTensor([[self.textnorm_dict[textnorm]]]).to(speech.device)
- ).repeat(speech.size(0), 1, 1)
- speech = torch.cat((textnorm_query, speech), dim=1)
- speech_lengths += 1
-
- event_emo_query = self.embed(torch.LongTensor([[1, 2]]).to(speech.device)).repeat(
- speech.size(0), 1, 1
- )
- input_query = torch.cat((language_query, event_emo_query), dim=1)
- speech = torch.cat((input_query, speech), dim=1)
- speech_lengths += 3
-
- # 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
- ctc_logits = self.ctc.log_softmax(encoder_out)
-
- results = []
- b, n, d = encoder_out.size()
- if isinstance(key[0], (list, tuple)):
- key = key[0]
- if len(key) < b:
- key = key * b
- for i in range(b):
- x = ctc_logits[i, : encoder_out_lens[i], :]
- yseq = x.argmax(dim=-1)
- yseq = torch.unique_consecutive(yseq, dim=-1)
- yseq = torch.tensor([self.sos] + yseq.tolist() + [self.eos], device=yseq.device)
- nbest_hyps = [Hypothesis(yseq=yseq)]
-
- 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
- text = tokenizer.decode(token_int)
-
- result_i = {"key": key[i], "text": text}
- results.append(result_i)
-
- if ibest_writer is not None:
- ibest_writer["token"][key[i]] = " ".join(token)
- ibest_writer["text"][key[i]] = text_postprocessed
-
- return results, meta_data
--
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