mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
354 lines
14 KiB
Python
354 lines
14 KiB
Python
import logging
|
|
from typing import Union, Dict, List, Tuple, Optional
|
|
|
|
import time
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from torch.cuda.amp import autocast
|
|
|
|
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
|
|
from funasr.models.ctc.ctc import CTC
|
|
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
|
|
from funasr.metrics.compute_acc import th_accuracy, compute_accuracy
|
|
# from funasr.models.e2e_asr_common import ErrorCalculator
|
|
from funasr.train_utils.device_funcs import force_gatherable
|
|
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
|
from funasr.utils import postprocess_utils
|
|
from funasr.utils.datadir_writer import DatadirWriter
|
|
from funasr.register import tables
|
|
|
|
|
|
@tables.register("model_classes", "LLMASR")
|
|
class LLMASR(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,
|
|
ctc: str = None,
|
|
ctc_conf: dict = None,
|
|
ctc_weight: float = 0.5,
|
|
llm: str = None,
|
|
llm_conf: dict = None,
|
|
adaptor: str = None,
|
|
adaptor_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)
|
|
|
|
# audio encoder
|
|
hub = encoder_conf.get("hub", None)
|
|
if hub == "funasr":
|
|
from funasr import AutoModel
|
|
from funasr.models.scama.utils import sequence_mask
|
|
init_param_path = encoder_conf.get("hub", "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
|
|
model = AutoModel(model=init_param_path, model_revision="v2.0.4")
|
|
frontend = model.kwargs.get("frontend")
|
|
model.model.decoder = None
|
|
|
|
self.model = model.model
|
|
self.frontend = frontend
|
|
self.mask_fn = sequence_mask
|
|
|
|
elif hub == "hf":
|
|
pass
|
|
else:
|
|
encoder_class = tables.encoder_classes.get(encoder)
|
|
encoder = encoder_class(input_size=input_size, **encoder_conf)
|
|
encoder_output_size = encoder.output_size()
|
|
|
|
# llm
|
|
hub = llm_conf.get("hub", "hf")
|
|
self.llm = None
|
|
if hub == "hf":
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
|
|
|
|
init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
init_param_path,
|
|
load_in_8bit=None,
|
|
device_map=None,
|
|
use_cache=None,
|
|
)
|
|
freeze_llm = llm_conf.get("freeze_llm", True)
|
|
if freeze_llm:
|
|
for name, param in model.named_parameters():
|
|
param.requires_grad = False
|
|
model.eval()
|
|
self.llm = model
|
|
|
|
# adaptor
|
|
adaptor_class = tables.adaptor_classes.get(adaptor)
|
|
adaptor = adaptor_class(**adaptor_conf)
|
|
|
|
self.adaptor = adaptor
|
|
|
|
|
|
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.criterion_att = LabelSmoothingLoss(
|
|
size=vocab_size,
|
|
padding_idx=ignore_id,
|
|
smoothing=lsm_weight,
|
|
normalize_length=length_normalized_loss,
|
|
)
|
|
#
|
|
# if report_cer or report_wer:
|
|
# self.error_calculator = ErrorCalculator(
|
|
# token_list, sym_space, sym_blank, report_cer, report_wer
|
|
# )
|
|
#
|
|
self.error_calculator = 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,
|
|
input_ids: torch.Tensor,
|
|
attention_mask:torch.Tensor,
|
|
labels_ids:torch.Tensor,
|
|
label_mask: torch.Tensor,
|
|
audio_mask:torch.Tensor,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
|
|
"""Encoder + Decoder + Calc loss
|
|
Args:
|
|
speech: (Batch, Length, ...)
|
|
speech_lengths: (Batch, )
|
|
text: (Batch, Length)
|
|
text_lengths: (Batch,)
|
|
"""
|
|
# import pdb;
|
|
# pdb.set_trace()
|
|
if len(text_lengths.size()) > 1:
|
|
text_lengths = text_lengths[:, 0]
|
|
if len(speech_lengths.size()) > 1:
|
|
speech_lengths = speech_lengths[:, 0]
|
|
|
|
batch_size = speech.shape[0]
|
|
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths, audio_mask)
|
|
|
|
# adaptor
|
|
encoder_out = self.adaptor(encoder_out)
|
|
|
|
if input_ids is not None:
|
|
input_ids[input_ids == -1] = 0
|
|
if hasattr(self.llm.model, "embed_tokens"):
|
|
inputs_embeds = self.llm.model.embed_tokens(input_ids)
|
|
elif hasattr(self.llm.model.model, "embed_tokens"):
|
|
inputs_embeds = self.llm.model.model.embed_tokens(input_ids)
|
|
else:
|
|
inputs_embeds = self.llm.model.model.model.embed_tokens(input_ids)
|
|
|
|
if audio_mask is not None:
|
|
batch_size, token_num, dims = inputs_embeds.shape
|
|
_, l, _ = encoder_out.shape
|
|
encoder_outs_pad = F.pad(encoder_out, (0, 0, token_num-l-1, 1, 0, 0), value=0.0)
|
|
inputs_embeds = encoder_outs_pad * audio_mask[:, :, None] + inputs_embeds * (~audio_mask[:, :, None])
|
|
inputs_embeds = F.pad(inputs_embeds[:, 1:, :], (0, 0, 0, 1, 0, 0), value=0.0)
|
|
|
|
model_outputs = self.llm(inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels)
|
|
loss = model_outputs.loss
|
|
|
|
acc_att = -1
|
|
if self.metric:
|
|
with torch.no_grad():
|
|
preds = torch.argmax(model_outputs.logits, -1)
|
|
acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
|
|
|
|
stats = {}
|
|
# Collect Attn branch stats
|
|
stats["acc"] = acc_att.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,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
|
|
audio_mask = kwargs.get("audio_mask")
|
|
audio_token_lengths = audio_mask.sum(-1)
|
|
|
|
batch = {"speech": speech, "speech_lengths": speech_lengths}
|
|
enc, enc_lens = self.model.encode(**batch)
|
|
enc_mask = self.mask_fn(enc_lens, enc.size(1), device=enc.device)[:, None, :]
|
|
pre_acoustic_embeds, pre_token_length, _, _ = self.model.predictor(enc,
|
|
mask=enc_mask,
|
|
target_label_length=audio_token_lengths,
|
|
)
|
|
|
|
return pre_acoustic_embeds, pre_token_length
|
|
|
|
def _calc_att_loss(
|
|
self,
|
|
encoder_out: torch.Tensor,
|
|
encoder_out_lens: torch.Tensor,
|
|
ys_pad: torch.Tensor,
|
|
ys_pad_lens: torch.Tensor,
|
|
):
|
|
ys_in_pad, ys_out_pad = add_sos_eos(ys_pad, self.sos, self.eos, self.ignore_id)
|
|
ys_in_lens = ys_pad_lens + 1
|
|
|
|
# 1. Forward decoder
|
|
decoder_out, _ = self.decoder(
|
|
encoder_out, encoder_out_lens, ys_in_pad, ys_in_lens
|
|
)
|
|
|
|
# 2. Compute attention loss
|
|
loss_att = self.criterion_att(decoder_out, ys_out_pad)
|
|
acc_att = th_accuracy(
|
|
decoder_out.view(-1, self.vocab_size),
|
|
ys_out_pad,
|
|
ignore_label=self.ignore_id,
|
|
)
|
|
|
|
# Compute cer/wer using attention-decoder
|
|
if self.training or self.error_calculator is None:
|
|
cer_att, wer_att = None, None
|
|
else:
|
|
ys_hat = decoder_out.argmax(dim=-1)
|
|
cer_att, wer_att = self.error_calculator(ys_hat.cpu(), ys_pad.cpu())
|
|
|
|
return loss_att, acc_att, cer_att, wer_att
|
|
|
|
|
|
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 self.beam_search is None:
|
|
logging.info("enable beam_search")
|
|
self.init_beam_search(**kwargs)
|
|
self.nbest = kwargs.get("nbest", 1)
|
|
|
|
meta_data = {}
|
|
if isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank": # fbank
|
|
speech, speech_lengths = data_in, data_lengths
|
|
if len(speech.shape) < 3:
|
|
speech = speech[None, :, :]
|
|
if speech_lengths is None:
|
|
speech_lengths = speech.shape[1]
|
|
else:
|
|
# extract fbank feats
|
|
time1 = time.perf_counter()
|
|
audio_sample_list = load_audio_text_image_video(data_in, fs=frontend.fs, audio_fs=kwargs.get("fs", 16000),
|
|
data_type=kwargs.get("data_type", "sound"),
|
|
tokenizer=tokenizer)
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
speech, speech_lengths = extract_fbank(audio_sample_list, data_type=kwargs.get("data_type", "sound"),
|
|
frontend=frontend)
|
|
time3 = time.perf_counter()
|
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
|
meta_data["batch_data_time"] = speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
|
|
|
speech = speech.to(device=kwargs["device"])
|
|
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
|
# Encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
if isinstance(encoder_out, tuple):
|
|
encoder_out = encoder_out[0]
|
|
|
|
# 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.tokens2text(token)
|
|
|
|
text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
|
|
result_i = {"key": key[i], "token": token, "text": text_postprocessed}
|
|
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
|
|
|