mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
1663 lines
60 KiB
Python
1663 lines
60 KiB
Python
import logging
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from dataclasses import dataclass
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from typing import Dict
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from typing import Iterable, Optional
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import types
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import time
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torch import Tensor
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from torch import nn
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from torch.cuda.amp import autocast
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from funasr.metrics.compute_acc import compute_accuracy
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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from funasr.train_utils.device_funcs import force_gatherable
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from . import whisper_lib as whisper
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.models.ctc.ctc import CTC
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from funasr.register import tables
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@tables.register("model_classes", "SenseVoice")
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class SenseVoice(nn.Module):
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def __init__(self, *args, **kwargs):
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super().__init__()
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dims = kwargs.get("dims", {})
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dims = whisper.model.ModelDimensions(**dims)
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model = whisper.model.Whisper(dims=dims)
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# encoder
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model.encoder.downsample_rate = kwargs.get("downsample_rate", 4)
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model.encoder.use_padmask = kwargs.get("use_padmask", True)
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from .encoder import sense_voice_encode_forward
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model.encoder.forward = types.MethodType(sense_voice_encode_forward, model.encoder)
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# decoder
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model.decoder.use_padmask = kwargs.get("use_padmask", True)
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from .decoder import sense_voice_decode_forward
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model.decoder.forward = types.MethodType(sense_voice_decode_forward, model.decoder)
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self.model = model
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self.encoder_output_size = self.model.dims.n_audio_state
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self.activation_checkpoint = kwargs.get("activation_checkpoint", False)
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self.ignore_id = kwargs.get("ignore_id", -1)
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self.vocab_size = kwargs.get("vocab_size", -1)
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self.length_normalized_loss = kwargs.get("length_normalized_loss", True)
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self.criterion_att = LabelSmoothingLoss(
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size=self.vocab_size,
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padding_idx=self.ignore_id,
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smoothing=kwargs.get("lsm_weight", 0.0),
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normalize_length=self.length_normalized_loss,
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)
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specaug = kwargs.get("specaug", None)
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if specaug is not None:
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specaug_class = tables.specaug_classes.get(specaug)
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specaug = specaug_class(**kwargs.get("specaug_conf", {}))
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self.specaug = specaug
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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**kwargs,
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):
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target_mask = kwargs.get("target_mask", None)
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# import pdb;
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# pdb.set_trace()
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if len(text_lengths.size()) > 1:
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text_lengths = text_lengths[:, 0]
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if len(speech_lengths.size()) > 1:
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speech_lengths = speech_lengths[:, 0]
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batch_size = speech.shape[0]
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if self.activation_checkpoint:
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from torch.utils.checkpoint import checkpoint
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encoder_out, encoder_out_lens = checkpoint(
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self.encode, speech, speech_lengths, use_reentrant=False
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)
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else:
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
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encoder_out, encoder_out_lens, text, text_lengths, target_mask=target_mask
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)
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loss = loss_att
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stats = {}
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stats["acc"] = acc_att
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stats["loss"] = torch.clone(loss.detach())
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stats["batch_size"] = batch_size
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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if self.length_normalized_loss:
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batch_size = int((text_lengths + 1).sum())
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loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
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return loss, stats, weight
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def encode(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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**kwargs,
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):
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"""Encoder. Note that this method is used by asr_inference.py
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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ind: int
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"""
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with autocast(False):
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# Data augmentation
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if self.specaug is not None and self.training:
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speech, speech_lengths = self.specaug(speech, speech_lengths)
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# Forward encoder
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encoder_out, encoder_out_lens = self.model.encoder(speech.permute(0, 2, 1), speech_lengths)
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return encoder_out, encoder_out_lens
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def _calc_att_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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**kwargs,
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):
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target_mask = kwargs.get("target_mask", None)
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stats = {}
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# 1. Forward decoder
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decoder_out = self.model.decoder(
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x=ys_pad, xa=encoder_out, hlens=encoder_out_lens, ys_in_lens=ys_pad_lens
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)
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# 2. Compute attention loss
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mask = torch.ones_like(ys_pad) * (-1)
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ys_pad_mask = (ys_pad * target_mask + mask * (1 - target_mask)).to(torch.int64)
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ys_pad_mask[ys_pad_mask == 0] = -1
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loss_att = self.criterion_att(decoder_out[:, :-1, :], ys_pad_mask[:, 1:])
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with torch.no_grad():
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preds = torch.argmax(decoder_out, -1)
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acc_att = compute_accuracy(
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preds[:, :-1], ys_pad_mask[:, 1:], ignore_label=self.ignore_id
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)
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return loss_att, acc_att, None, None
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def inference(
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self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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**kwargs,
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):
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if kwargs.get("batch_size", 1) > 1:
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raise NotImplementedError("batch decoding is not implemented")
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if frontend is None and not hasattr(self, "frontend"):
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frontend_class = tables.frontend_classes.get("WhisperFrontend")
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frontend = frontend_class(
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n_mels=self.model.dims.n_mels, do_pad_trim=kwargs.get("do_pad_trim", True)
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)
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self.frontend = frontend
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else:
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frontend = frontend if frontend is not None else self.frontend
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meta_data = {}
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if (
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isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
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): # fbank
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speech, speech_lengths = data_in, data_lengths
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if len(speech.shape) < 3:
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speech = speech[None, :, :]
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if speech_lengths is None:
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speech_lengths = speech.shape[1]
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else:
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# extract fbank feats
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time1 = time.perf_counter()
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audio_sample_list = load_audio_text_image_video(
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data_in,
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fs=frontend.fs if hasattr(frontend, "fs") else 16000,
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audio_fs=kwargs.get("fs", 16000),
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data_type=kwargs.get("data_type", "sound"),
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tokenizer=tokenizer,
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)
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time2 = time.perf_counter()
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meta_data["load_data"] = f"{time2 - time1:0.3f}"
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speech, speech_lengths = extract_fbank(
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audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
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)
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time3 = time.perf_counter()
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meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
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frame_shift = frontend.frame_shift if hasattr(frontend, "frame_shift") else 10
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lfr_n = frontend.lfr_n if hasattr(frontend, "lfr_n") else 1
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meta_data["batch_data_time"] = speech_lengths.sum().item() * frame_shift * lfr_n / 1000
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speech = speech.to(device=kwargs["device"])[0, :, :]
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speech_lengths = speech_lengths.to(device=kwargs["device"])
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DecodingOptions = kwargs.get("DecodingOptions", {})
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task = DecodingOptions.get("task", "ASR")
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if isinstance(task, str):
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task = [task]
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task = "".join([f"<|{x}|>" for x in task])
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initial_prompt = kwargs.get("initial_prompt", f"<|startoftranscript|>{task}")
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DecodingOptions["initial_prompt"] = initial_prompt
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language = DecodingOptions.get("language", None)
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language = None if language == "auto" else language
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DecodingOptions["language"] = language
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DecodingOptions["vocab_path"] = kwargs["tokenizer_conf"].get("vocab_path", None)
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if "without_timestamps" not in DecodingOptions:
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DecodingOptions["without_timestamps"] = True
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options = whisper.DecodingOptions(**DecodingOptions)
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result = whisper.decode(self.model, speech, options)
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text = f"{result.text}"
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results = []
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result_i = {"key": key[0], "text": text}
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results.append(result_i)
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return results, meta_data
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@tables.register("model_classes", "SenseVoiceRWKV")
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class SenseVoiceRWKV(nn.Module):
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def __init__(self, *args, **kwargs):
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super().__init__()
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dims = kwargs.get("dims", {})
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dims = whisper.model.ModelDimensions(**dims)
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model = whisper.model.Whisper(dims=dims)
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# encoder
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model.encoder.downsample_rate = kwargs.get("downsample_rate", 4)
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model.encoder.use_padmask = kwargs.get("use_padmask", True)
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from .encoder import sense_voice_encode_forward
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model.encoder.forward = types.MethodType(sense_voice_encode_forward, model.encoder)
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# decoder
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del model.decoder
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decoder = kwargs.get("decoder", "SenseVoiceDecoder")
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decoder_class = tables.decoder_classes.get(decoder)
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decoder = decoder_class(
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n_vocab=dims.n_vocab,
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n_ctx=dims.n_text_ctx,
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n_state=dims.n_text_state,
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n_head=dims.n_text_head,
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n_layer=dims.n_text_layer,
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**kwargs.get("decoder_conf"),
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)
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model.decoder = decoder
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self.model = model
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self.encoder_output_size = self.model.dims.n_audio_state
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self.activation_checkpoint = kwargs.get("activation_checkpoint", False)
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self.ignore_id = kwargs.get("ignore_id", -1)
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self.vocab_size = kwargs.get("vocab_size", -1)
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self.length_normalized_loss = kwargs.get("length_normalized_loss", True)
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self.criterion_att = LabelSmoothingLoss(
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size=self.vocab_size,
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padding_idx=self.ignore_id,
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smoothing=kwargs.get("lsm_weight", 0.0),
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normalize_length=self.length_normalized_loss,
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)
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specaug = kwargs.get("specaug", None)
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if specaug is not None:
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specaug_class = tables.specaug_classes.get(specaug)
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specaug = specaug_class(**kwargs.get("specaug_conf", {}))
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self.specaug = specaug
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def forward(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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text: torch.Tensor,
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text_lengths: torch.Tensor,
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**kwargs,
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):
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target_mask = kwargs.get("target_mask", None)
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# import pdb;
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# pdb.set_trace()
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if len(text_lengths.size()) > 1:
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text_lengths = text_lengths[:, 0]
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if len(speech_lengths.size()) > 1:
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speech_lengths = speech_lengths[:, 0]
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batch_size, frames, _ = speech.shape
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_, text_tokens = text.shape
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if self.activation_checkpoint:
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from torch.utils.checkpoint import checkpoint
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encoder_out, encoder_out_lens = checkpoint(
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self.encode, speech, speech_lengths, use_reentrant=False
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)
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else:
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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loss_att, acc_att, cer_att, wer_att = self._calc_att_loss(
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encoder_out, encoder_out_lens, text, text_lengths, target_mask=target_mask
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)
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loss = loss_att
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stats = {}
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stats["acc"] = acc_att
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stats["loss"] = torch.clone(loss.detach())
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stats["batch_size"] = batch_size
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stats["batch_size_x_frames"] = frames * batch_size
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stats["batch_size_real_frames"] = speech_lengths.sum().item()
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stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"]
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stats["batch_size_x_tokens"] = text_tokens * batch_size
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stats["batch_size_real_tokens"] = text_lengths.sum().item()
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stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"]
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stats["batch_size_x_frames_plus_tokens"] = (text_tokens + frames) * batch_size
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
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if self.length_normalized_loss:
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batch_size = int((text_lengths + 1).sum())
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loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
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return loss, stats, weight
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def encode(
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self,
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speech: torch.Tensor,
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speech_lengths: torch.Tensor,
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**kwargs,
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):
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"""Encoder. Note that this method is used by asr_inference.py
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
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ind: int
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"""
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with autocast(False):
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# Data augmentation
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if self.specaug is not None and self.training:
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speech, speech_lengths = self.specaug(speech, speech_lengths)
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# Forward encoder
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encoder_out, encoder_out_lens = self.model.encoder(speech.permute(0, 2, 1), speech_lengths)
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return encoder_out, encoder_out_lens
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def _calc_att_loss(
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self,
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encoder_out: torch.Tensor,
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encoder_out_lens: torch.Tensor,
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ys_pad: torch.Tensor,
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ys_pad_lens: torch.Tensor,
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**kwargs,
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):
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target_mask = kwargs.get("target_mask", None)
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stats = {}
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# 1. Forward decoder
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# ys_pad: [sos, task, lid, text, eos]
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decoder_out = self.model.decoder(
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x=ys_pad, xa=encoder_out, hlens=encoder_out_lens, ys_in_lens=ys_pad_lens
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)
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# 2. Compute attention loss
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mask = torch.ones_like(ys_pad) * (-1) # [sos, task, lid, text, eos]: [-1, -1, -1, -1]
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ys_pad_mask = (ys_pad * target_mask + mask * (1 - target_mask)).to(
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torch.int64
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) # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1] + [-1, -1, 0, 0, 0]
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ys_pad_mask[ys_pad_mask == 0] = -1 # [-1, -1, lid, text, eos]
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# decoder_out: [sos, task, lid, text]
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# ys_pad_mask: [-1, lid, text, eos]
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loss_att = self.criterion_att(decoder_out[:, :-1, :], ys_pad_mask[:, 1:])
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with torch.no_grad():
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preds = torch.argmax(decoder_out, -1)
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acc_att = compute_accuracy(
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preds[:, :-1], ys_pad_mask[:, 1:], ignore_label=self.ignore_id
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)
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return loss_att, acc_att, None, None
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def init_beam_search(
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self,
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**kwargs,
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):
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from .search import BeamSearch
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from funasr.models.transformer.scorers.length_bonus import LengthBonus
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# 1. Build ASR model
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scorers = {}
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scorers.update(
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decoder=self.model.decoder,
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length_bonus=LengthBonus(self.vocab_size),
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)
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weights = dict(
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decoder=1.0,
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ctc=0.0,
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lm=0.0,
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ngram=0.0,
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length_bonus=kwargs.get("penalty", 0.0),
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)
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beam_search = BeamSearch(
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beam_size=kwargs.get("beam_size", 5),
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weights=weights,
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scorers=scorers,
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sos=None,
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eos=None,
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vocab_size=self.vocab_size,
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token_list=None,
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pre_beam_score_key="full",
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)
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self.beam_search = beam_search
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|
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def inference(
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self,
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data_in,
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data_lengths=None,
|
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key: list = None,
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tokenizer=None,
|
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frontend=None,
|
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**kwargs,
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):
|
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if kwargs.get("batch_size", 1) > 1:
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raise NotImplementedError("batch decoding is not implemented")
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|
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# init beamsearch
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if not hasattr(self, "beam_search") or self.beam_search is None:
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logging.info("enable beam_search")
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self.init_beam_search(**kwargs)
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self.nbest = kwargs.get("nbest", 1)
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|
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if frontend is None and not hasattr(self, "frontend"):
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frontend_class = tables.frontend_classes.get("WhisperFrontend")
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frontend = frontend_class(
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n_mels=self.model.dims.n_mels, do_pad_trim=kwargs.get("do_pad_trim", True)
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)
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self.frontend = frontend
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else:
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frontend = frontend if frontend is not None else self.frontend
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|
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meta_data = {}
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if (
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isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
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): # fbank
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speech, speech_lengths = data_in, data_lengths
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if len(speech.shape) < 3:
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speech = speech[None, :, :]
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if speech_lengths is None:
|
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speech_lengths = speech.shape[1]
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else:
|
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# extract fbank feats
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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,
|
|
)
|
|
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
|
|
)
|
|
|
|
# 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
|
|
|
|
|
|
@tables.register("model_classes", "SenseVoiceFSMN")
|
|
class SenseVoiceFSMN(nn.Module):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__()
|
|
|
|
dims = kwargs.get("dims", {})
|
|
dims = whisper.model.ModelDimensions(**dims)
|
|
model = whisper.model.Whisper(dims=dims)
|
|
|
|
# encoder
|
|
model.encoder.downsample_rate = kwargs.get("downsample_rate", 4)
|
|
model.encoder.use_padmask = kwargs.get("use_padmask", True)
|
|
from .encoder import sense_voice_encode_forward
|
|
|
|
model.encoder.forward = types.MethodType(sense_voice_encode_forward, model.encoder)
|
|
|
|
# decoder
|
|
del model.decoder
|
|
decoder = kwargs.get("decoder", "SenseVoiceDecoder")
|
|
decoder_class = tables.decoder_classes.get(decoder)
|
|
decoder = decoder_class(
|
|
n_vocab=dims.n_vocab,
|
|
n_ctx=dims.n_text_ctx,
|
|
n_state=dims.n_text_state,
|
|
n_head=dims.n_text_head,
|
|
n_layer=dims.n_text_layer,
|
|
**kwargs.get("decoder_conf"),
|
|
)
|
|
model.decoder = decoder
|
|
|
|
self.model = model
|
|
|
|
self.encoder_output_size = self.model.dims.n_audio_state
|
|
|
|
self.activation_checkpoint = kwargs.get("activation_checkpoint", False)
|
|
self.ignore_id = kwargs.get("ignore_id", -1)
|
|
self.vocab_size = dims.n_vocab
|
|
self.length_normalized_loss = kwargs.get("length_normalized_loss", True)
|
|
self.criterion_att = LabelSmoothingLoss(
|
|
size=self.vocab_size,
|
|
padding_idx=self.ignore_id,
|
|
smoothing=kwargs.get("lsm_weight", 0.0),
|
|
normalize_length=self.length_normalized_loss,
|
|
)
|
|
|
|
specaug = kwargs.get("specaug", None)
|
|
if specaug is not None:
|
|
specaug_class = tables.specaug_classes.get(specaug)
|
|
specaug = specaug_class(**kwargs.get("specaug_conf", {}))
|
|
self.specaug = specaug
|
|
|
|
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,
|
|
):
|
|
"""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
|
|
encoder_out, encoder_out_lens = self.model.encoder(speech.permute(0, 2, 1), speech_lengths)
|
|
|
|
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.model.decoder(
|
|
x=ys_pad, xa=encoder_out, hlens=encoder_out_lens, ys_in_lens=ys_pad_lens
|
|
)
|
|
# decoder_out, _ = self.model.decoder(encoder_out, encoder_out_lens, ys_pad, ys_pad_lens)
|
|
# 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.model.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
|
|
|
|
|
|
@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)
|
|
|
|
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.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.length_normalized_loss = length_normalized_loss
|
|
self.beam_search = None
|
|
self.activation_checkpoint = kwargs.get("activation_checkpoint", False)
|
|
self.encoder_output_size = encoder_output_size
|
|
|
|
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
|
|
ys_pad[ys_pad == -1] = 0
|
|
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])
|
|
|
|
sos = kwargs.get("model_conf").get("sos")
|
|
if isinstance(sos, str):
|
|
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")
|
|
else:
|
|
language = DecodingOptions.get("language", None)
|
|
language = None if language == "auto" else language
|
|
initial_prompt = kwargs.get("initial_prompt", f"{task}")
|
|
initial_prompt_lid = (
|
|
f"{initial_prompt}<|{language}|>" if language is not None else initial_prompt
|
|
)
|
|
initial_prompt_lid_int = tokenizer.encode(initial_prompt_lid, allowed_special="all")
|
|
sos_int = [sos] + initial_prompt_lid_int
|
|
eos = kwargs.get("model_conf").get("eos")
|
|
if isinstance(eos, str):
|
|
eos_int = tokenizer.encode(eos, allowed_special="all")
|
|
else:
|
|
eos_int = [eos]
|
|
|
|
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, :, :], 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
|
|
|
|
|
|
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
|