from dataclasses import dataclass
|
from typing import Dict
|
from typing import Iterable, Optional
|
import types
|
import time
|
import numpy as np
|
import torch
|
import torch.nn.functional as F
|
from torch import Tensor
|
from torch import nn
|
from torch.cuda.amp import autocast
|
from funasr.metrics.compute_acc import compute_accuracy
|
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
|
from funasr.train_utils.device_funcs import force_gatherable
|
from . import whisper_lib as whisper
|
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
|
from funasr.register import tables
|
|
|
@tables.register("model_classes", "SenseVoice")
|
class SenseVoice(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
|
model.decoder.use_padmask = kwargs.get("use_padmask", True)
|
from .decoder import sense_voice_decode_forward
|
|
model.decoder.forward = types.MethodType(sense_voice_decode_forward, model.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 = kwargs.get("vocab_size", -1)
|
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 = speech.shape[0]
|
|
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
|
|
# 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
|
)
|
|
# 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 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")
|
|
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,
|
)
|
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}")
|
DecodingOptions["initial_prompt"] = initial_prompt
|
|
language = DecodingOptions.get("language", None)
|
language = None if language == "auto" else language
|
DecodingOptions["language"] = language
|
|
DecodingOptions["vocab_path"] = kwargs["tokenizer_conf"].get("vocab_path", None)
|
|
if "without_timestamps" not in DecodingOptions:
|
DecodingOptions["without_timestamps"] = True
|
|
options = whisper.DecodingOptions(**DecodingOptions)
|
|
result = whisper.decode(self.model, speech, options)
|
text = f"{result.text}"
|
results = []
|
result_i = {"key": key[0], "text": text}
|
|
results.append(result_i)
|
|
return results, meta_data
|
|
|
@tables.register("model_classes", "SenseVoiceRWKV")
|
class SenseVoiceRWKV(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 = kwargs.get("vocab_size", -1)
|
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 = speech.shape[0]
|
|
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
|
|
# 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
|
)
|
|
# 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 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")
|
|
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,
|
)
|
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}")
|
DecodingOptions["initial_prompt"] = initial_prompt
|
|
language = DecodingOptions.get("language", None)
|
language = None if language == "auto" else language
|
DecodingOptions["language"] = language
|
|
DecodingOptions["vocab_path"] = kwargs["tokenizer_conf"].get("vocab_path", None)
|
|
if "without_timestamps" not in DecodingOptions:
|
DecodingOptions["without_timestamps"] = True
|
|
options = whisper.DecodingOptions(**DecodingOptions)
|
|
result = whisper.decode(self.model, speech, options)
|
text = f"{result.text}"
|
results = []
|
result_i = {"key": key[0], "text": text}
|
|
results.append(result_i)
|
|
return results, meta_data
|
