From 7d06f581dbe603e98fe10bd296ce0ef3494d7a86 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期六, 11 五月 2024 19:40:29 +0800
Subject: [PATCH] sensevoice sanm
---
funasr/models/sense_voice/model.py | 416 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 416 insertions(+), 0 deletions(-)
diff --git a/funasr/models/sense_voice/model.py b/funasr/models/sense_voice/model.py
index 56e61e7..a633a8d 100644
--- a/funasr/models/sense_voice/model.py
+++ b/funasr/models/sense_voice/model.py
@@ -966,3 +966,419 @@
ibest_writer["text"][key[i]] = text
return results, meta_data
+
+
+@tables.register("model_classes", "SenseVoiceSANM")
+class SenseVoiceSANM(nn.Module):
+
+ def __init__(
+ self,
+ specaug: str = None,
+ specaug_conf: dict = None,
+ normalize: str = None,
+ normalize_conf: dict = None,
+ encoder: str = None,
+ encoder_conf: dict = None,
+ decoder: str = None,
+ decoder_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,
+ 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,
+ encoder_output_size=encoder_output_size,
+ **decoder_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.decoder = decoder
+
+ self.criterion_att = LabelSmoothingLoss(
+ size=vocab_size,
+ padding_idx=ignore_id,
+ smoothing=lsm_weight,
+ normalize_length=length_normalized_loss,
+ )
+
+ self.error_calculator = None
+
+ self.share_embedding = share_embedding
+ if self.share_embedding:
+ self.decoder.embed = None
+
+ 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,
+ ):
+ target_mask = kwargs.get("target_mask", None)
+
+ # 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, frames, _ = speech.shape
+ _, text_tokens = text.shape
+
+ if self.activation_checkpoint:
+ from torch.utils.checkpoint import checkpoint
+
+ encoder_out, encoder_out_lens = checkpoint(
+ self.encode, speech, speech_lengths, use_reentrant=False
+ )
+ 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
+ )
+
+ loss = loss_att
+ stats = {}
+ stats["acc"] = acc_att
+ stats["loss"] = torch.clone(loss.detach())
+ stats["batch_size"] = batch_size
+ stats["batch_size_x_frames"] = frames * batch_size
+ stats["batch_size_real_frames"] = speech_lengths.sum().item()
+ stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"]
+ stats["batch_size_x_tokens"] = text_tokens * batch_size
+ stats["batch_size_real_tokens"] = text_lengths.sum().item()
+ stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"]
+ stats["batch_size_x_frames_plus_tokens"] = (text_tokens + frames) * batch_size
+
+ # 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
+ """
+ with autocast(False):
+
+ # Data augmentation
+ if self.specaug is not None and self.training:
+ speech, speech_lengths = self.specaug(speech, speech_lengths)
+
+ # Forward encoder
+ # feats: (Batch, Length, Dim)
+ # -> encoder_out: (Batch, Length2, Dim2)
+
+ encoder_out, encoder_out_lens, _ = self.encoder(speech, speech_lengths)
+ if isinstance(encoder_out, (tuple, list)):
+ encoder_out = encoder_out[0]
+
+ return encoder_out, encoder_out_lens
+
+ def _calc_att_loss(
+ self,
+ encoder_out: torch.Tensor,
+ encoder_out_lens: torch.Tensor,
+ ys_pad: torch.Tensor,
+ ys_pad_lens: torch.Tensor,
+ **kwargs,
+ ):
+ target_mask = kwargs.get("target_mask", None)
+ stats = {}
+
+ # 1. Forward decoder
+ decoder_out = self.decoder(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
+ if isinstance(decoder_out, (list, tuple)):
+ decoder_out = decoder_out[0]
+
+ # 2. Compute attention loss
+ mask = torch.ones_like(ys_pad) * (-1)
+ ys_pad_mask = (ys_pad * target_mask + mask * (1 - target_mask)).to(torch.int64)
+ ys_pad_mask[ys_pad_mask == 0] = -1
+ loss_att = self.criterion_att(decoder_out[:, :-1, :], ys_pad_mask[:, 1:])
+
+ with torch.no_grad():
+ preds = torch.argmax(decoder_out, -1)
+ acc_att = compute_accuracy(
+ preds[:, :-1], ys_pad_mask[:, 1:], ignore_label=self.ignore_id
+ )
+
+ return loss_att, acc_att, None, None
+
+ def init_beam_search(
+ self,
+ **kwargs,
+ ):
+ from .search import BeamSearch
+
+ from funasr.models.transformer.scorers.length_bonus import LengthBonus
+
+ # 1. Build ASR model
+ scorers = {}
+
+ scorers.update(
+ decoder=self.decoder,
+ length_bonus=LengthBonus(self.vocab_size),
+ )
+
+ weights = dict(
+ decoder=1.0,
+ ctc=0.0,
+ lm=0.0,
+ ngram=0.0,
+ length_bonus=kwargs.get("penalty", 0.0),
+ )
+ beam_search = BeamSearch(
+ beam_size=kwargs.get("beam_size", 5),
+ weights=weights,
+ scorers=scorers,
+ sos=None,
+ eos=None,
+ vocab_size=self.vocab_size,
+ token_list=None,
+ pre_beam_score_key="full",
+ )
+
+ self.beam_search = beam_search
+
+ 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")
+
+ # init beamsearch
+ if not hasattr(self, "beam_search") or self.beam_search is None:
+ logging.info("enable beam_search")
+ self.init_beam_search(**kwargs)
+ self.nbest = kwargs.get("nbest", 1)
+
+ if frontend is None and not hasattr(self, "frontend"):
+ frontend_class = tables.frontend_classes.get("WhisperFrontend")
+ frontend = frontend_class(
+ n_mels=self.model.dims.n_mels, do_pad_trim=kwargs.get("do_pad_trim", True)
+ )
+ self.frontend = frontend
+ else:
+ frontend = frontend if frontend is not None else self.frontend
+
+ 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 if hasattr(frontend, "fs") else 16000,
+ audio_fs=kwargs.get("fs", 16000),
+ data_type=kwargs.get("data_type", "sound"),
+ tokenizer=tokenizer,
+ )
+
+ if (
+ isinstance(kwargs.get("data_type", None), (list, tuple))
+ and len(kwargs.get("data_type", [])) > 1
+ ):
+ audio_sample_list, text_token_int_list = audio_sample_list
+ text_token_int = text_token_int_list[0]
+ else:
+ text_token_int = None
+
+ 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}"
+ frame_shift = frontend.frame_shift if hasattr(frontend, "frame_shift") else 10
+ lfr_n = frontend.lfr_n if hasattr(frontend, "lfr_n") else 1
+ meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift * lfr_n / 1000
+
+ speech = speech.to(device=kwargs["device"])[0, :, :]
+ speech_lengths = speech_lengths.to(device=kwargs["device"])
+
+ DecodingOptions = kwargs.get("DecodingOptions", {})
+ task = DecodingOptions.get("task", "ASR")
+ if isinstance(task, str):
+ task = [task]
+ task = "".join([f"<|{x}|>" for x in task])
+ initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")
+
+ language = DecodingOptions.get("language", None)
+ language = None if language == "auto" else language
+
+ sos = f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
+ sos_int = tokenizer.encode(sos, allowed_special="all")
+ eos = kwargs.get("model_conf").get("eos")
+ eos_int = tokenizer.encode(eos, allowed_special="all")
+ self.beam_search.sos = sos_int
+ self.beam_search.eos = eos_int[0]
+
+ # Paramterts for rich decoding
+ self.beam_search.emo_unk = tokenizer.encode(
+ DecodingOptions.get("emo_unk_token", "<|SPECIAL_TOKEN_1|>"), allowed_special="all"
+ )[0]
+ self.beam_search.emo_unk_score = 1
+ self.beam_search.emo_tokens = tokenizer.encode(
+ DecodingOptions.get("emo_target_tokens", "<|HAPPY|><|SAD|><|ANGRY|>"),
+ allowed_special="all",
+ )
+ self.beam_search.emo_scores = DecodingOptions.get("emo_target_threshold", [0.1, 0.1, 0.1])
+
+ self.beam_search.event_bg_token = tokenizer.encode(
+ DecodingOptions.get("gain_tokens_bg", "<|Speech|><|BGM|><|Applause|><|Laughter|>"),
+ allowed_special="all",
+ )
+ self.beam_search.event_ed_token = tokenizer.encode(
+ DecodingOptions.get("gain_tokens_ed", "<|/Speech|><|/BGM|><|/Applause|><|/Laughter|>"),
+ allowed_special="all",
+ )
+ self.beam_search.event_score_ga = DecodingOptions.get("gain_tokens_score", [1, 1, 1, 1])
+
+ encoder_out, encoder_out_lens = self.encode(
+ speech[None, :, :].permute(0, 2, 1), speech_lengths
+ )
+
+ if text_token_int is not None:
+ i = 0
+ results = []
+ 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"1best_recog"]
+
+ # 1. Forward decoder
+ ys_pad = torch.tensor(sos_int + text_token_int, dtype=torch.int64).to(kwargs["device"])[
+ None, :
+ ]
+ ys_pad_lens = torch.tensor([len(sos_int + text_token_int)], dtype=torch.int64).to(
+ kwargs["device"]
+ )[None, :]
+ decoder_out = self.model.decoder(
+ x=ys_pad, xa=encoder_out, hlens=encoder_out_lens, ys_in_lens=ys_pad_lens
+ )
+
+ token_int = decoder_out.argmax(-1)[0, :].tolist()
+ 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
+ return results, meta_data
+
+ # c. Passed the encoder result and the beam search
+ nbest_hyps = self.beam_search(
+ x=encoder_out[0],
+ maxlenratio=kwargs.get("maxlenratio", 0.0),
+ minlenratio=kwargs.get("minlenratio", 0.0),
+ )
+
+ 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.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
+
+ return results, meta_data
--
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