import logging
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from typing import Union, Dict, List, Tuple, Optional
|
|
import time
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.cuda.amp import autocast
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from funasr.models.scama.utils import sequence_mask
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from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
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from funasr.models.ctc.ctc import CTC
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from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
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from funasr.metrics.compute_acc import th_accuracy, compute_accuracy
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from funasr.metrics.common import ErrorCalculator
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from funasr.train_utils.device_funcs import force_gatherable
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from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
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from funasr.utils import postprocess_utils
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.register import tables
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@tables.register("model_classes", "LLMASR")
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class LLMASR(nn.Module):
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""" """
|
|
def __init__(
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self,
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specaug: str = None,
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specaug_conf: dict = None,
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normalize: str = None,
|
normalize_conf: dict = None,
|
audio_encoder: str = None,
|
audio_encoder_conf: dict = None,
|
audio_adaptor: str = None,
|
audio_adaptor_conf: dict = None,
|
decoder: str = None,
|
decoder_conf: dict = None,
|
ctc: str = None,
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ctc_conf: dict = None,
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ctc_weight: float = 0.5,
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llm: str = None,
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llm_conf: dict = None,
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input_size: int = 80,
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vocab_size: int = -1,
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ignore_id: int = -1,
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blank_id: int = 0,
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sos: int = 1,
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eos: int = 2,
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lsm_weight: float = 0.0,
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length_normalized_loss: bool = False,
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report_cer: bool = True,
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report_wer: bool = True,
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sym_space: str = "<space>",
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sym_blank: str = "<blank>",
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# extract_feats_in_collect_stats: bool = True,
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share_embedding: bool = False,
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# preencoder: Optional[AbsPreEncoder] = None,
|
# postencoder: Optional[AbsPostEncoder] = None,
|
**kwargs,
|
):
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super().__init__()
|
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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(**specaug_conf)
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if normalize is not None:
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normalize_class = tables.normalize_classes.get(normalize)
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normalize = normalize_class(**normalize_conf)
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# audio encoder
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hub = audio_encoder_conf.get("hub", None)
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if hub == "ms":
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from funasr import AutoModel
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model = AutoModel(model=audio_encoder, model_revision="master")
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# frontend = model.kwargs.get("frontend")
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audio_encoder_output_size = model.model.encoder_output_size
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audio_encoder = model.model.model.encoder
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# self.frontend = frontend
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elif hub == "hf":
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pass
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else:
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encoder_class = tables.encoder_classes.get(audio_encoder)
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audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf)
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audio_encoder_output_size = audio_encoder.output_size()
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freeze = audio_encoder_conf.get("freeze", True)
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if freeze:
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for name, param in audio_encoder.named_parameters():
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param.requires_grad = False
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audio_encoder.eval()
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self.audio_encoder = audio_encoder
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# llm
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hub = llm_conf.get("hub", "hf")
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self.llm = None
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if hub == "hf":
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from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
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init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
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model = AutoModelForCausalLM.from_pretrained(
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init_param_path,
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load_in_8bit=None,
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device_map=None,
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use_cache=None,
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)
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freeze = llm_conf.get("freeze", True)
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if freeze:
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for name, param in model.named_parameters():
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param.requires_grad = False
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model.eval()
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self.llm = model
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# adaptor
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adaptor_class = tables.adaptor_classes.get(audio_adaptor)
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audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
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audio_adaptor = adaptor_class(**audio_adaptor_conf)
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self.audio_adaptor = audio_adaptor
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self.blank_id = blank_id
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self.sos = sos if sos is not None else vocab_size - 1
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self.eos = eos if eos is not None else vocab_size - 1
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self.vocab_size = vocab_size
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self.ignore_id = ignore_id
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self.specaug = specaug
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self.normalize = normalize
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self.criterion_att = LabelSmoothingLoss(
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size=vocab_size,
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padding_idx=ignore_id,
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smoothing=lsm_weight,
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normalize_length=length_normalized_loss,
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)
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self.error_calculator = None
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self.length_normalized_loss = length_normalized_loss
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self.beam_search = None
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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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input_ids: torch.Tensor,
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attention_mask: torch.Tensor,
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labels_ids: torch.Tensor,
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label_mask: torch.Tensor,
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audio_mask: torch.Tensor,
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**kwargs,
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) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""Encoder + Decoder + Calc loss
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Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
|
text: (Batch, Length)
|
text_lengths: (Batch,)
|
"""
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# import pdb;
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# pdb.set_trace()
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if len(text_lengths.size()) > 1:
|
text_lengths = text_lengths[:, 0]
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if len(speech_lengths.size()) > 1:
|
speech_lengths = speech_lengths[:, 0]
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batch_size = speech.shape[0]
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# audio encoder
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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# audio_adaptor
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encoder_out = self.audio_adaptor(encoder_out)
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input_ids[input_ids == -1] = 0
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input_ids[input_ids == -100] = 0
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if hasattr(self.llm.model, "embed_tokens"):
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inputs_embeds = self.llm.model.embed_tokens(input_ids)
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elif hasattr(self.llm.model.model, "embed_tokens"):
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inputs_embeds = self.llm.model.model.embed_tokens(input_ids)
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else:
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inputs_embeds = self.llm.model.model.model.embed_tokens(input_ids)
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if audio_mask is not None:
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batch_size, token_num, dims = inputs_embeds.shape
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_, l, _ = encoder_out.shape
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# [audio, bos, prompt, input, pad]
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encoder_outs_pad = F.pad(encoder_out, (0, 0, 0, token_num - l, 0, 0), value=0.0)
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inputs_embeds = encoder_outs_pad * audio_mask[:, :, None] + inputs_embeds * (
|
1.0 - audio_mask[:, :, None]
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)
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model_outputs = self.llm(
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inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
|
)
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loss = model_outputs.loss
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stats = {}
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with torch.no_grad():
|
preds = torch.argmax(model_outputs.logits, -1)
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acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
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stats["acc"] = acc_att
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stats["loss"] = torch.clone(loss.detach())
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# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
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,
|
):
|
speech = speech.permute(0, 2, 1)
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res = self.audio_encoder(speech)
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if isinstance(res, (list, tuple)):
|
encoder_out, encoder_out_lens = res[0], res[1]
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else:
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encoder_out, encoder_out_lens = res, speech_lengths
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return encoder_out, encoder_out_lens
|
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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,
|
tokenizer=None,
|
frontend=None,
|
**kwargs,
|
):
|
|
prompt = kwargs.get("prompt", "Transcribe speech to text.")
|
|
if kwargs.get("batch_size", 1) > 1:
|
raise NotImplementedError("batch decoding is not implemented")
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|
meta_data = {}
|
if (
|
isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
|
): # fbank
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speech, speech_lengths = data_in, data_lengths
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if len(speech.shape) < 3:
|
speech = speech[None, :, :]
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if speech_lengths is None:
|
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,
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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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time2 = time.perf_counter()
|
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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time3 = time.perf_counter()
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meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
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meta_data["batch_data_time"] = (
|
speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
)
|
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speech = speech.to(device=kwargs["device"])
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speech_lengths = speech_lengths.to(device=kwargs["device"])
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# Encoder
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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|
# adaptor
|
encoder_out = self.audio_adaptor(encoder_out)
|
|
prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(prompt)
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prompt_ids = tokenizer.encode(prompt_pre)
|
prompt_length = len(prompt_ids)
|
prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(kwargs["device"])
|
|
if hasattr(self.llm.model, "embed_tokens"):
|
inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
|
elif hasattr(self.llm.model.model, "embed_tokens"):
|
inputs_embeds = self.llm.model.model.embed_tokens(prompt_ids)
|
else:
|
inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)
|
|
inputs_embeds = torch.cat(
|
(inputs_embeds[None, :, :], encoder_out), dim=1
|
) # [prompt, audio]
|
attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(
|
kwargs["device"]
|
)
|
|
preds = self.llm.generate(
|
inputs_embeds=inputs_embeds,
|
max_length=kwargs.get("max_length", 200),
|
max_new_tokens=kwargs.get("max_new_tokens", 200),
|
num_beams=kwargs.get("num_beams", 4),
|
do_sample=kwargs.get("do_sample", False),
|
min_length=kwargs.get("min_length", 1),
|
top_p=kwargs.get("top_p", 1.0),
|
repetition_penalty=kwargs.get("repetition_penalty", 1.0),
|
length_penalty=kwargs.get("length_penalty", 1.0),
|
temperature=kwargs.get("temperature", 1.0),
|
attention_mask=attention_mask,
|
bos_token_id=tokenizer.bos_token_id,
|
eos_token_id=tokenizer.eos_token_id,
|
pad_token_id=tokenizer.pad_token_id,
|
)
|
|
text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
|
|
text = text[0].split(": ")[-1]
|
text = text.strip()
|
|
# preds = torch.argmax(model_outputs.logits, -1)
|
|
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"{0 + 1}best_recog"]
|
|
results = []
|
result_i = {"key": key[0], "text": text}
|
results.append(result_i)
|
|
if ibest_writer is not None:
|
ibest_writer["text"][key[0]] = text
|
|
return results, meta_data
|
|
|
@tables.register("model_classes", "LLMASR2")
|
class LLMASR2(nn.Module):
|
""" """
|
|
def __init__(
|
self,
|
specaug: str = None,
|
specaug_conf: dict = None,
|
normalize: str = None,
|
normalize_conf: dict = None,
|
audio_encoder: str = None,
|
audio_encoder_conf: dict = None,
|
audio_adaptor: str = None,
|
audio_adaptor_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,
|
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__()
|
|
# audio encoder
|
hub = audio_encoder_conf.get("hub", None)
|
if hub == "ms":
|
from funasr import AutoModel
|
|
model = AutoModel(model=audio_encoder, model_revision="master")
|
# frontend = model.kwargs.get("frontend")
|
audio_encoder_output_size = model.model.encoder_output_size
|
|
audio_encoder = model.model.model.encoder
|
|
# self.frontend = frontend
|
|
elif hub == "hf":
|
pass
|
else:
|
encoder_class = tables.encoder_classes.get(audio_encoder)
|
audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf)
|
audio_encoder_output_size = audio_encoder.output_size()
|
freeze = audio_encoder_conf.get("freeze", True)
|
if freeze:
|
for name, param in audio_encoder.named_parameters():
|
param.requires_grad = False
|
audio_encoder.eval()
|
|
self.audio_encoder = audio_encoder
|
|
# 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_conf.get("freeze", True)
|
if freeze:
|
for name, param in model.named_parameters():
|
param.requires_grad = False
|
model.eval()
|
self.llm = model
|
|
# adaptor
|
adaptor_class = tables.adaptor_classes.get(audio_adaptor)
|
audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
|
audio_adaptor = adaptor_class(**audio_adaptor_conf)
|
|
self.audio_adaptor = audio_adaptor
|
|
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,
|
input_ids: torch.Tensor,
|
attention_mask: torch.Tensor,
|
labels_ids: torch.Tensor,
|
fbank_beg: torch.Tensor,
|
fbank_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(speech_lengths.size()) > 1:
|
speech_lengths = speech_lengths[:, 0]
|
|
batch_size, frames, _ = speech.shape
|
|
# audio encoder
|
encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)
|
|
# audio_adaptor
|
encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)
|
|
input_ids[input_ids < 0] = 0
|
inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)
|
|
batch_size, token_num, dims = inputs_embeds.shape
|
fbank_mask[fbank_mask < 0] = 0
|
fbank_fake_lens = fbank_mask.sum(-1).to(torch.int32)
|
# _, l, _ = encoder_out.shape
|
for batch_idx in range(batch_size):
|
|
fbank_fake_len = fbank_fake_lens[batch_idx].item()
|
fbank_beg_idx = fbank_beg[batch_idx, 0].item()
|
min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
|
fbank_fake_len = encoder_out_lens[batch_idx].item()
|
min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
|
try:
|
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
|
batch_idx, :min_len, :
|
]
|
except Exception as e:
|
logging.error(f"{str(e)}, {traceback.format_exc()}")
|
logging.info(
|
f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}"
|
)
|
fbank_fake_len = encoder_out_lens[batch_idx].item()
|
min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
|
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
|
batch_idx, :min_len, :
|
]
|
|
labels_ids[labels_ids == -1] = -100
|
model_outputs = self.llm(
|
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
|
)
|
loss = model_outputs.loss
|
|
stats = {}
|
with torch.no_grad():
|
preds = torch.argmax(model_outputs.logits, -1)
|
acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
|
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"] = token_num * batch_size
|
stats["batch_size_real_tokens"] = attention_mask.sum().item()
|
stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"]
|
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
if self.length_normalized_loss:
|
batch_size = int((labels_ids > 0 + 1).sum())
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
return loss, stats, weight
|
|
def inference(
|
self,
|
data_in,
|
data_lengths=None,
|
key: list = None,
|
tokenizer=None,
|
frontend=None,
|
**kwargs,
|
):
|
|
prompt = kwargs.get("prompt", "Transcribe speech to text.")
|
|
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"])
|
|
# Encoder
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
# adaptor
|
encoder_out = self.audio_adaptor(encoder_out)
|
|
prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(prompt)
|
prompt_ids = tokenizer.encode(prompt_pre)
|
prompt_length = len(prompt_ids)
|
prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(kwargs["device"])
|
|
if hasattr(self.llm.model, "embed_tokens"):
|
inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
|
elif hasattr(self.llm.model.model, "embed_tokens"):
|
inputs_embeds = self.llm.model.model.embed_tokens(prompt_ids)
|
else:
|
inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)
|
|
inputs_embeds = torch.cat(
|
(inputs_embeds[None, :, :], encoder_out), dim=1
|
) # [prompt, audio]
|
attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(
|
kwargs["device"]
|
)
|
|
preds = self.llm.generate(
|
inputs_embeds=inputs_embeds,
|
max_length=kwargs.get("max_length", 200),
|
max_new_tokens=kwargs.get("max_new_tokens", 200),
|
num_beams=kwargs.get("num_beams", 4),
|
do_sample=kwargs.get("do_sample", False),
|
min_length=kwargs.get("min_length", 1),
|
top_p=kwargs.get("top_p", 1.0),
|
repetition_penalty=kwargs.get("repetition_penalty", 1.0),
|
length_penalty=kwargs.get("length_penalty", 1.0),
|
temperature=kwargs.get("temperature", 1.0),
|
attention_mask=attention_mask,
|
bos_token_id=tokenizer.bos_token_id,
|
eos_token_id=tokenizer.eos_token_id,
|
pad_token_id=tokenizer.pad_token_id,
|
)
|
|
text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
|
|
text = text[0].split(": ")[-1]
|
text = text.strip()
|
|
# preds = torch.argmax(model_outputs.logits, -1)
|
|
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"{0 + 1}best_recog"]
|
|
results = []
|
result_i = {"key": key[0], "text": text}
|
results.append(result_i)
|
|
if ibest_writer is not None:
|
ibest_writer["text"][key[0]] = text
|
|
return results, meta_data
|
