import logging
|
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
|
|
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.models.e2e_asr_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.models.paraformer.cif_predictor import mae_loss
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from funasr.utils.datadir_writer import DatadirWriter
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from funasr.register import tables
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|
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@tables.register("model_classes", "LLMASRNAR")
|
class LLMASRNAR(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,
|
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,
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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__()
|
|
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:
|
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 = encoder_conf.get("hub", None)
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if hub == "funasr":
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from funasr import AutoModel
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|
init_param_path = encoder_conf.get(
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"init_param_path",
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"iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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)
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model = AutoModel(model=init_param_path, model_revision="master")
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# frontend = model.kwargs.get("frontend")
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model.model.decoder = None
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self.audio_encoder = model.model
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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(encoder)
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encoder = encoder_class(input_size=input_size, **encoder_conf)
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encoder_output_size = encoder.output_size()
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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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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(adaptor)
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adaptor = adaptor_class(**adaptor_conf)
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self.adaptor = 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.encoder = encoder
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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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#
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# if report_cer or report_wer:
|
# self.error_calculator = ErrorCalculator(
|
# token_list, sym_space, sym_blank, report_cer, report_wer
|
# )
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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
|
|
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,
|
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
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"""Encoder + Decoder + Calc loss
|
Args:
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speech: (Batch, Length, ...)
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speech_lengths: (Batch, )
|
text: (Batch, Length)
|
text_lengths: (Batch,)
|
"""
|
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, audio_mask=audio_mask)
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# adaptor
|
encoder_out = self.adaptor(encoder_out)
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if input_ids is not None:
|
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"):
|
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:
|
batch_size, token_num, dims = inputs_embeds.shape
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_, l, _ = encoder_out.shape
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encoder_outs_pad = F.pad(encoder_out, (0, 0, token_num - l - 1, 1, 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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inputs_embeds = F.pad(inputs_embeds[:, 1:, :], (0, 0, 0, 1, 0, 0), value=0.0)
|
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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:
|
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
|
|
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,
|
):
|
|
audio_mask = kwargs.get("audio_mask", None)
|
audio_token_lengths = audio_mask.sum(-1) if audio_mask is not None else None
|
text_token_int = kwargs.get("text_token_int", None)
|
if audio_token_lengths is None:
|
audio_token_lengths = torch.tensor([len(text_token_int)], dtype=torch.int64)
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batch = {"speech": speech, "speech_lengths": speech_lengths}
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enc, enc_lens = self.audio_encoder.encode(**batch)
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with autocast(False):
|
enc_mask = sequence_mask(enc_lens, enc.size(1), device=enc.device)[:, None, :]
|
pre_acoustic_embeds, pre_token_length, _, _ = self.audio_encoder.predictor(
|
enc,
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mask=enc_mask,
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target_label_length=audio_token_lengths,
|
)
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return pre_acoustic_embeds, pre_token_length
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|
def inference(
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self,
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data_in,
|
data_lengths=None,
|
key: list = None,
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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 (
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isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
|
): # fbank
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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,
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fs=frontend.fs,
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audio_fs=kwargs.get("fs", 16000),
|
data_type=kwargs.get("data_type", "sound"),
|
tokenizer=None,
|
)
|
if len(kwargs.get("data_type", [])) > 1:
|
audio_sample_list, text_token_int_list = audio_sample_list
|
text_token_int = text_token_int_list[0].replace(" ", "")
|
text_token_int = tokenizer.encode(text_token_int)
|
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}"
|
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, text_token_int=text_token_int
|
)
|
|
# adaptor
|
encoder_out = self.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"]
|
)
|
|
# model_outputs = 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
|
# )
|
|
model_outputs = self.llm(
|
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=None
|
)
|
preds = torch.argmax(model_outputs.logits, -1)
|
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", "LLMASRNARPrompt")
|
class LLMASRNARPrompt(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 = 0.0,
|
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,
|
predictor_weight: int = 1.0,
|
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
|
|
init_param_path = encoder_conf.get(
|
"init_param_path",
|
"iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
|
)
|
model = AutoModel(model=init_param_path, model_revision="master")
|
# frontend = model.kwargs.get("frontend")
|
model.model.decoder = None
|
|
self.audio_encoder = model.model
|
# self.frontend = frontend
|
self.predictor_weight = predictor_weight
|
|
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_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(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,
|
)
|
self.criterion_pre = mae_loss(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
|
if ctc_weight > 0.0:
|
if ctc_conf is None:
|
ctc_conf = {}
|
|
ctc = CTC(odim=vocab_size, encoder_output_size=adaptor_conf["encoder_dim"], **ctc_conf)
|
self.ctc_weight = ctc_weight
|
self.ctc = ctc
|
|
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]
|
|
stats = {}
|
# audio encoder
|
outs = self.encode(speech, speech_lengths, audio_mask=audio_mask)
|
enc, enc_lens = outs[0], outs[1]
|
encoder_out, encoder_out_lens, loss_pre = outs[2], outs[3], outs[4]
|
|
# decoder: CTC branch
|
|
if self.ctc_weight != 0.0:
|
loss_ctc, cer_ctc = self._calc_ctc_loss(enc, enc_lens, text, text_lengths)
|
|
# Collect CTC branch stats
|
stats["loss_ctc"] = torch.clone(loss_ctc.detach()) if loss_ctc is not None else None
|
|
# adaptor
|
encoder_out = self.adaptor(encoder_out)
|
|
if input_ids is not None:
|
input_ids[input_ids == -1] = 0
|
input_ids[input_ids == -100] = 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:
|
# inputs_embeds： [bos, prompt, input, pad, target]
|
prompt_bos_length = kwargs.get("prompt_bos_length", None)
|
assert prompt_bos_length is not None
|
prompt_bos_length = prompt_bos_length[0].item()
|
batch_size, token_num, dims = inputs_embeds.shape
|
_, l, _ = encoder_out.shape
|
encoder_outs_pad = F.pad(
|
encoder_out,
|
(0, 0, prompt_bos_length, token_num - prompt_bos_length - l, 0, 0),
|
value=0.0,
|
)
|
inputs_embeds = encoder_outs_pad * audio_mask[:, :, None] + inputs_embeds * (
|
1.0 - audio_mask[:, :, None]
|
)
|
inputs_embeds = F.pad(
|
inputs_embeds[:, 1:, :], (0, 0, 0, 1, 0, 0), value=0.0
|
) # [prompt, input, pad, target, 0.0]
|
|
# labels_ids: [bos, prompt, input, target, eos] -> [-1, -1, input, target, eos]
|
# loss:
|
# inputs_embeds[:-1] -> [prompt, input, pad, target]
|
# labels_ids[1:] -> [prompt, input, target, eos] -> [-1, input, target, eos];
|
model_outputs = self.llm(
|
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
|
)
|
loss_llm = model_outputs.loss
|
stats["loss_llm"] = torch.clone(loss_llm.detach())
|
if self.ctc_weight > 0.0:
|
loss_llm = self.ctc_weight * loss_ctc + loss_llm
|
loss = loss_llm + loss_pre * self.predictor_weight
|
|
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_pre"] = torch.clone(loss_pre.detach())
|
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,
|
):
|
|
audio_mask = kwargs.get("audio_mask", None)
|
audio_token_lengths = audio_mask.sum(-1) if audio_mask is not None else None
|
text_token_int = kwargs.get("text_token_int", None)
|
if audio_token_lengths is None and text_token_int is not None:
|
audio_token_lengths = torch.tensor([len(text_token_int)], dtype=torch.int64)
|
|
batch = {"speech": speech, "speech_lengths": speech_lengths}
|
enc, enc_lens = self.audio_encoder.encode(**batch)
|
with autocast(False):
|
enc_mask = sequence_mask(enc_lens, enc.size(1), device=enc.device)[:, None, :]
|
pre_acoustic_embeds, pre_token_length, _, _ = self.audio_encoder.predictor(
|
enc,
|
mask=enc_mask,
|
target_label_length=audio_token_lengths,
|
)
|
loss_pre = 0.0
|
if audio_token_lengths is not None:
|
loss_pre = self.criterion_pre(
|
audio_token_lengths.type_as(pre_token_length), pre_token_length
|
)
|
|
return enc, enc_lens, pre_acoustic_embeds, pre_token_length, loss_pre
|
|
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,
|
):
|
|
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=None,
|
)
|
if len(kwargs.get("data_type", [])) > 1:
|
audio_sample_list, text_token_int_list = audio_sample_list
|
text_token_int = text_token_int_list[0]
|
text_token_int = tokenizer.encode(text_token_int)
|
if text_token_int[0] == tokenizer.bos_token_id:
|
text_token_int = text_token_int[1:]
|
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}"
|
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
|
res = self.encode(speech, speech_lengths, text_token_int=text_token_int)
|
encoder_out = res[0]
|
|
# adaptor
|
encoder_out = self.adaptor(encoder_out)
|
|
prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(prompt)
|
prompt_ids = tokenizer.encode(prompt_pre)
|
if prompt_ids[0] == tokenizer.bos_token_id:
|
prompt_ids = prompt_ids[1:]
|
# prompt_ids = prompt_ids + [tokenizer.pad_token_id]
|
prompt_length = len(prompt_ids)
|
prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(kwargs["device"])
|
pad = torch.tensor([tokenizer.pad_token_id], dtype=torch.int64).to(kwargs["device"])
|
|
if hasattr(self.llm.model, "embed_tokens"):
|
inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
|
pad = self.llm.model.embed_tokens(pad)
|
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, pad[None, :, :]), dim=1) # [prompt, audio, pad]
|
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"]
|
)
|
|
# model_outputs = 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
|
# )
|
|
model_outputs = self.llm(
|
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=None
|
)
|
preds = torch.argmax(model_outputs.logits, -1)
|
text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
|
|
text = text[0].split(":")[-1]
|
text = text.strip()
|
if text.startswith("Please\n "):
|
text = text.replace("Please\n ", "")
|
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
|
