mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
2427 lines
95 KiB
Python
2427 lines
95 KiB
Python
import logging
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import os.path
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import torchaudio
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from typing import Union, Dict, List, Tuple, Optional
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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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import numpy as np
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import re
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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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from funasr.train_utils.device_funcs import to_device
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from funasr.models.transformer.utils.nets_utils import make_pad_mask, pad_list
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from funasr.train_utils.set_all_random_seed import set_all_random_seed
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import traceback
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dtype_map = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}
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@tables.register("model_classes", "LLMASR")
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class LLMASR(nn.Module):
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""" """
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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,
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normalize_conf: dict = None,
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audio_encoder: str = None,
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audio_encoder_conf: dict = None,
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audio_adaptor: str = None,
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audio_adaptor_conf: dict = None,
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decoder: str = None,
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decoder_conf: dict = None,
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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,
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# postencoder: Optional[AbsPostEncoder] = None,
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**kwargs,
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):
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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, )
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text: (Batch, Length)
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text_lengths: (Batch,)
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"""
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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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# 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 * (
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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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)
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loss = model_outputs.loss
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stats = {}
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with torch.no_grad():
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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
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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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speech = speech.permute(0, 2, 1)
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res = self.audio_encoder(speech)
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if isinstance(res, (list, tuple)):
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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,
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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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prompt = kwargs.get("prompt", "Transcribe speech to text.")
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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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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,
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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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meta_data["batch_data_time"] = (
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speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
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)
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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
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encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
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# adaptor
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encoder_out = self.audio_adaptor(encoder_out)
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prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(prompt)
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prompt_ids = tokenizer.encode(prompt_pre)
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prompt_length = len(prompt_ids)
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prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(kwargs["device"])
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if hasattr(self.llm.model, "embed_tokens"):
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inputs_embeds = self.llm.model.embed_tokens(prompt_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(prompt_ids)
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else:
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inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)
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inputs_embeds = torch.cat(
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(inputs_embeds[None, :, :], encoder_out), dim=1
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) # [prompt, audio]
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attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(
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kwargs["device"]
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)
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preds = self.llm.generate(
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inputs_embeds=inputs_embeds,
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max_length=kwargs.get("max_length", 200),
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max_new_tokens=kwargs.get("max_new_tokens", 200),
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num_beams=kwargs.get("num_beams", 4),
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do_sample=kwargs.get("do_sample", False),
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min_length=kwargs.get("min_length", 1),
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top_p=kwargs.get("top_p", 1.0),
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repetition_penalty=kwargs.get("repetition_penalty", 1.0),
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length_penalty=kwargs.get("length_penalty", 1.0),
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temperature=kwargs.get("temperature", 1.0),
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attention_mask=attention_mask,
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bos_token_id=tokenizer.bos_token_id,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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)
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text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
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text = text[0].split(": ")[-1]
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text = text.strip()
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# preds = torch.argmax(model_outputs.logits, -1)
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ibest_writer = None
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if kwargs.get("output_dir") is not None:
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if not hasattr(self, "writer"):
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self.writer = DatadirWriter(kwargs.get("output_dir"))
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ibest_writer = self.writer[f"{0 + 1}best_recog"]
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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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if ibest_writer is not None:
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ibest_writer["text"][key[0]] = text
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return results, meta_data
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@tables.register("model_classes", "LLMASR2")
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class LLMASR2(nn.Module):
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""" """
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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,
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normalize_conf: dict = None,
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audio_encoder: str = None,
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audio_encoder_conf: dict = None,
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audio_adaptor: str = None,
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audio_adaptor_conf: dict = None,
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decoder: str = None,
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decoder_conf: dict = None,
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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,
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# postencoder: Optional[AbsPostEncoder] = None,
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**kwargs,
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):
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super().__init__()
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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 = (
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model.model.model.encoder if hasattr(model.model, "model") else model.model.encoder
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)
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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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freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1))
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# if freeze_layer_num > 0:
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# freeze_layer_num = range(freeze_layer_num)
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if freeze:
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for name, param in audio_encoder.named_parameters():
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if freeze_layer_num > 0:
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idx = re.search(r"\.\d+\.", name)
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if idx is not None:
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beg, end = idx.regs[0]
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layer_id = int(name[beg + 1 : end - 1])
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if layer_id < freeze_layer_num:
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param.requires_grad = False
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elif "ln_post." not in name:
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param.requires_grad = False
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else:
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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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self.llm = None
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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_dtype = llm_conf.get("llm_dtype", "fp32")
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self.llm = model.to(dtype_map[self.llm_dtype])
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llm_dim = model.get_input_embeddings().weight.shape[-1]
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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_conf["llm_dim"] = llm_dim
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audio_adaptor = adaptor_class(**audio_adaptor_conf)
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init_param_path = audio_adaptor_conf.get("init_param_path", None)
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if init_param_path is not None:
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src_state = torch.load(init_param_path, map_location="cpu")
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flag = audio_adaptor.load_state_dict(src_state, strict=False)
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logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}")
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self.audio_adaptor = audio_adaptor
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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,
|
|
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
|
|
|
|
with torch.cuda.amp.autocast(enabled=False):
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, 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)
|
|
|
|
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}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens[batch_idx].item()}"
|
|
)
|
|
fbank_fake_len = encoder_out_lens[batch_idx].item()
|
|
min_len = min(fbank_fake_len, min_len)
|
|
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
|
|
batch_idx, :min_len, :
|
|
]
|
|
|
|
with torch.cuda.amp.autocast(
|
|
enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype]
|
|
):
|
|
labels_ids[labels_ids == -1] = -100
|
|
attention_mask[attention_mask < 0] = 0
|
|
model_outputs = self.llm(
|
|
inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]),
|
|
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 encode(self, speech, speech_lengths):
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)
|
|
|
|
return encoder_out, encoder_out_lens
|
|
|
|
def data_template(self, data):
|
|
system, user, assistant = [], [], []
|
|
for i, item in enumerate(data):
|
|
role = item["role"]
|
|
content = item["content"]
|
|
if role == "system":
|
|
system.append(content)
|
|
elif role == "user":
|
|
user.append(content)
|
|
elif role == "assistant":
|
|
assistant.append(content)
|
|
|
|
system = system * len(user)
|
|
|
|
contents = {
|
|
"system": system,
|
|
"user": user,
|
|
"assistant": assistant,
|
|
}
|
|
|
|
return contents
|
|
|
|
def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs):
|
|
|
|
system = contents["system"]
|
|
user = contents["user"]
|
|
assistant = contents["assistant"]
|
|
pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
|
|
input_ids, labels, source_ids, target_ids, fbank, fbank_lens, fbank_mask, fbank_beg = (
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
)
|
|
|
|
for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)):
|
|
|
|
source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
|
|
|
|
splits = pattern.split(source_input)
|
|
source_ids_i = []
|
|
fbank_mask_i = []
|
|
fbank_beg_i = []
|
|
fbank_lens_i = []
|
|
# target_ids_i = []
|
|
for k, sub_str in enumerate(splits):
|
|
if not sub_str.startswith("<|startofspeech|>"):
|
|
sub_token = tokenizer.encode(sub_str)
|
|
source_ids_i += sub_token
|
|
fbank_mask_i += [0] * len(sub_token)
|
|
else:
|
|
sub_str = sub_str.replace("<|startofspeech|>", "").replace(
|
|
"<|endofspeech|>", ""
|
|
)
|
|
if sub_str.startswith("!"):
|
|
try:
|
|
time1 = time.perf_counter()
|
|
data_src = load_audio_text_image_video(sub_str[1:], fs=frontend.fs)
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
except Exception as e:
|
|
logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")
|
|
|
|
speech, speech_lengths = extract_fbank(
|
|
data_src,
|
|
data_type=kwargs.get("data_type", "sound"),
|
|
frontend=frontend,
|
|
is_final=True,
|
|
) # speech: [b, T, d]
|
|
|
|
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
|
|
)
|
|
|
|
if hasattr(frontend, "permute") and not frontend.permute:
|
|
# if kwargs.get("permute", True):
|
|
speech = speech.permute(0, 2, 1)
|
|
|
|
if (
|
|
kwargs.get("dataset_conf", {}).get("audio_encoder_downsample_rate", 1)
|
|
== 4
|
|
):
|
|
olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
|
|
olens = 1 + (olens - 3 + 2 * 1) // 2
|
|
elif (
|
|
kwargs.get("dataset_conf", {}).get("audio_encoder_downsample_rate", 1)
|
|
== 1
|
|
):
|
|
olens = speech_lengths[0].item()
|
|
|
|
sub_token_len = (olens - 1) // kwargs.get("dataset_conf", {}).get(
|
|
"audio_adaptor_downsample_rate", 1
|
|
) + 1
|
|
sub_token = [0] * sub_token_len
|
|
fbank_beg_i = [len(source_ids_i)]
|
|
source_ids_i += sub_token
|
|
fbank_mask_i += [1] * len(sub_token)
|
|
|
|
source_mask = [-100] * len(source_ids_i)
|
|
target_out = f"{target_out}<|im_end|>"
|
|
target_ids = tokenizer.encode(target_out)
|
|
input_ids += source_ids_i + target_ids
|
|
labels += source_mask + target_ids
|
|
fbank_mask += fbank_mask_i
|
|
fbank_beg.append(fbank_beg_i)
|
|
|
|
input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length]
|
|
attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
|
|
labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length]
|
|
source_ids = torch.tensor(source_ids_i, dtype=torch.int64)
|
|
target_ids = torch.tensor(target_ids, dtype=torch.int64)
|
|
|
|
fbank = speech[0, :, :]
|
|
fbank_lens = speech_lengths
|
|
fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
|
|
fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)
|
|
|
|
output = {
|
|
"speech": fbank[None, :, :],
|
|
"speech_lengths": fbank_lens[:, None],
|
|
"fbank_mask": fbank_mask[None, :],
|
|
"fbank_beg": fbank_beg[None,],
|
|
"input_ids": input_ids[None, :],
|
|
"attention_mask": attention_mask[None, :],
|
|
"labels_ids": labels[None, :],
|
|
"source_ids": source_ids[None, :],
|
|
"target_ids": target_ids[None, :],
|
|
}
|
|
|
|
return output
|
|
|
|
def inference(
|
|
self,
|
|
data_in,
|
|
data_lengths=None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
frontend=None,
|
|
**kwargs,
|
|
):
|
|
|
|
meta_data = {}
|
|
prompt = kwargs.get("prompt", None)
|
|
|
|
if kwargs.get("batch_size", 1) > 1:
|
|
raise NotImplementedError("batch decoding is not implemented")
|
|
|
|
contents = self.data_template(data_in[0])
|
|
output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs)
|
|
batch = to_device(output, kwargs["device"])
|
|
|
|
# audio encoder
|
|
speech = batch["speech"]
|
|
speech_lengths = batch["speech_lengths"][:, 0]
|
|
# fp16
|
|
if kwargs.get("fp16", False):
|
|
speech = speech.to(torch.float16)
|
|
elif kwargs.get("bf16", False):
|
|
speech = speech.to(torch.bfloat16)
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
# audio_adaptor
|
|
encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)
|
|
|
|
input_ids = batch["input_ids"]
|
|
source_ids = batch["source_ids"]
|
|
if not kwargs.get("tearchforing", False):
|
|
input_ids = source_ids
|
|
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_beg = batch["fbank_beg"]
|
|
for batch_idx in range(batch_size):
|
|
|
|
min_len = encoder_out_lens[batch_idx].item()
|
|
fbank_beg_idx = fbank_beg[batch_idx]
|
|
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
|
|
batch_idx, :min_len, :
|
|
]
|
|
|
|
llm_dtype = kwargs.get("llm_dtype", "fp32")
|
|
if llm_dtype == "fp32":
|
|
llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype
|
|
llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype
|
|
|
|
with torch.cuda.amp.autocast(
|
|
enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype]
|
|
):
|
|
label = contents["assistant"][0]
|
|
self.llm = self.llm.to(dtype_map[llm_dtype])
|
|
inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype])
|
|
|
|
if not kwargs.get("tearchforing", False):
|
|
|
|
generated_ids = self.llm.generate(
|
|
inputs_embeds=inputs_embeds, max_new_tokens=kwargs.get("max_length", 512)
|
|
)
|
|
# generated_ids = [
|
|
# output_ids[len(input_id) :]
|
|
# for input_id, output_ids in zip(input_ids, generated_ids)
|
|
# ]
|
|
response = tokenizer.batch_decode(
|
|
generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True)
|
|
)[0]
|
|
|
|
loss = None
|
|
else:
|
|
|
|
labels_ids = batch["labels_ids"]
|
|
labels_ids[labels_ids == -1] = -100
|
|
attention_mask = batch.get("attention_mask", None)
|
|
# attention_mask = attention_mask.to(dtype_map[llm_dtype])
|
|
model_outputs = self.llm(
|
|
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
|
|
)
|
|
|
|
preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :]
|
|
response = tokenizer.batch_decode(
|
|
preds,
|
|
add_special_tokens=False,
|
|
skip_special_tokens=kwargs.get("skip_special_tokens", True),
|
|
)[0]
|
|
loss = model_outputs.loss.item()
|
|
|
|
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 = []
|
|
response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response)
|
|
result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label}
|
|
if loss is not None:
|
|
result_i["loss"] = loss
|
|
results.append(result_i)
|
|
|
|
if ibest_writer is not None:
|
|
ibest_writer["text"][key[0]] = response
|
|
ibest_writer["label"][key[0]] = label
|
|
ibest_writer["text_tn"][key[0]] = response_clean
|
|
|
|
return results, meta_data
|
|
|
|
|
|
@tables.register("model_classes", "LLMASR3")
|
|
class LLMASR3(LLMASR2):
|
|
""" """
|
|
|
|
def __init__(
|
|
self,
|
|
*args,
|
|
**kwargs,
|
|
):
|
|
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def encode(self, speech, speech_lengths):
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.audio_encoder(speech, speech_lengths)
|
|
return encoder_out, encoder_out_lens
|
|
|
|
|
|
@tables.register("model_classes", "LLMASR4")
|
|
class LLMASR4(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 if hasattr(model.model, "model") else 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)
|
|
freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1))
|
|
# if freeze_layer_num > 0:
|
|
# freeze_layer_num = range(freeze_layer_num)
|
|
|
|
if freeze:
|
|
for name, param in audio_encoder.named_parameters():
|
|
if freeze_layer_num > 0:
|
|
idx = re.search(r"\.\d+\.", name)
|
|
if idx is not None:
|
|
beg, end = idx.regs[0]
|
|
layer_id = int(name[beg + 1 : end - 1])
|
|
if layer_id < freeze_layer_num:
|
|
param.requires_grad = False
|
|
elif "ln_post." not in name:
|
|
param.requires_grad = False
|
|
else:
|
|
param.requires_grad = False
|
|
|
|
audio_encoder.eval()
|
|
|
|
self.audio_encoder = audio_encoder
|
|
|
|
# llm
|
|
self.llm = None
|
|
|
|
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_dtype = llm_conf.get("llm_dtype", "fp32")
|
|
self.llm = model.to(dtype_map[self.llm_dtype])
|
|
llm_dim = model.get_input_embeddings().weight.shape[-1]
|
|
|
|
# adaptor
|
|
adaptor_class = tables.adaptor_classes.get(audio_adaptor)
|
|
audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
|
|
audio_adaptor_conf["llm_dim"] = llm_dim
|
|
audio_adaptor = adaptor_class(**audio_adaptor_conf)
|
|
init_param_path = audio_adaptor_conf.get("init_param_path", None)
|
|
if init_param_path is not None:
|
|
src_state = torch.load(init_param_path, map_location="cpu")
|
|
flag = audio_adaptor.load_state_dict(src_state, strict=False)
|
|
logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}")
|
|
|
|
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 = None,
|
|
speech_lengths: torch.Tensor = None,
|
|
input_ids: torch.Tensor = None,
|
|
attention_mask: torch.Tensor = None,
|
|
labels_ids: torch.Tensor = None,
|
|
fbank_beg: torch.Tensor = None,
|
|
fbank_mask: torch.Tensor = None,
|
|
**kwargs,
|
|
):
|
|
"""Encoder + Decoder + Calc loss
|
|
Args:
|
|
speech: (Batch, Length, ...)
|
|
speech_lengths: (Batch, )
|
|
text: (Batch, Length)
|
|
text_lengths: (Batch,)
|
|
"""
|
|
# import pdb
|
|
#
|
|
# pdb.set_trace()
|
|
input_ids[input_ids < 0] = 0
|
|
inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)
|
|
if speech is not None:
|
|
if len(speech_lengths.size()) > 1:
|
|
speech_lengths = speech_lengths[:, 0]
|
|
|
|
batch_size_speech, frames, _ = speech.shape
|
|
batch_size, token_num = input_ids.shape
|
|
|
|
with torch.cuda.amp.autocast(enabled=False):
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
# audio_adaptor
|
|
encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)
|
|
|
|
batch_size, token_num, dims = inputs_embeds.shape
|
|
fake_token_len = kwargs.get("fake_token_len")
|
|
fake_token_len[fake_token_len < 0] = 0
|
|
fbank_beg[fbank_beg < 0] = 0
|
|
|
|
speech_idx = 0
|
|
for batch_idx in range(batch_size):
|
|
|
|
for turn_id in range(fbank_beg.shape[1]):
|
|
fbank_beg_idx = fbank_beg[batch_idx, turn_id].item()
|
|
if fbank_beg_idx > 0:
|
|
speech_token_len = fake_token_len[batch_idx, turn_id]
|
|
speech_token = encoder_out[speech_idx, :speech_token_len, :]
|
|
|
|
try:
|
|
inputs_embeds[
|
|
batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, :
|
|
] = speech_token
|
|
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}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}"
|
|
)
|
|
# import pdb;
|
|
# pdb.set_trace()
|
|
speech_token_len = encoder_out_lens[speech_idx].item()
|
|
speech_token = encoder_out[speech_idx, :speech_token_len, :]
|
|
inputs_embeds[
|
|
batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, :
|
|
] = speech_token
|
|
|
|
speech_idx += 1
|
|
|
|
with torch.cuda.amp.autocast(
|
|
enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype]
|
|
):
|
|
labels_ids[labels_ids == -1] = -100
|
|
attention_mask[attention_mask < 0] = 0
|
|
model_outputs = self.llm(
|
|
inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]),
|
|
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_speech"] = batch_size_speech
|
|
stats["batch_size_x_frames"] = frames * batch_size_speech
|
|
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"]
|
|
|
|
dialog_turns = (fbank_beg > 0).sum(-1)
|
|
dialog_turns_max = torch.max(dialog_turns).int().item()
|
|
dialog_turns_avg = dialog_turns.sum().item() / batch_size
|
|
stats["dialog_turns_max"] = dialog_turns_max
|
|
stats["dialog_turns_avg"] = dialog_turns_avg
|
|
|
|
# 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 encode(self, speech, speech_lengths):
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)
|
|
|
|
return encoder_out, encoder_out_lens
|
|
|
|
def data_template(self, data):
|
|
system, user, assistant = [], [], []
|
|
for i, item in enumerate(data):
|
|
role = item["role"]
|
|
content = item["content"]
|
|
if role == "system":
|
|
system.append(content)
|
|
elif role == "user":
|
|
if "audio" in item:
|
|
audio = item["audio"]
|
|
content = [content, audio]
|
|
user.append(content)
|
|
elif role == "assistant":
|
|
assistant.append(content)
|
|
|
|
system = system * len(user)
|
|
|
|
contents = {
|
|
"system": system,
|
|
"user": user,
|
|
"assistant": assistant,
|
|
}
|
|
|
|
return contents
|
|
|
|
def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs):
|
|
|
|
system = contents["system"]
|
|
user = contents["user"]
|
|
assistant = contents["assistant"]
|
|
pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
|
|
|
|
input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg, fake_token_len = (
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
)
|
|
input_source_ids = []
|
|
for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)):
|
|
if i >= kwargs.get("multiturn_num_max", 5):
|
|
break
|
|
if len(input_ids) > kwargs.get("max_token_length", 1500):
|
|
|
|
break
|
|
if isinstance(user_prompt, (list, tuple)):
|
|
user_prompt, audio = user_prompt
|
|
if i == 0:
|
|
source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
|
|
else:
|
|
source_input = f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
|
|
|
|
splits = pattern.split(source_input)
|
|
source_ids = []
|
|
fbank_i = []
|
|
fbank_mask_i = []
|
|
fake_token_len_i = 0
|
|
fbank_beg_i = -1
|
|
fbank_lens_i = []
|
|
speech, speech_lengths = [], []
|
|
for k, sub_str in enumerate(splits):
|
|
if not sub_str.startswith("<|startofspeech|>"):
|
|
sub_token = tokenizer.encode(sub_str)
|
|
source_ids += sub_token
|
|
fbank_mask_i += [0] * len(sub_token)
|
|
else:
|
|
sub_str = sub_str.replace("<|startofspeech|>", "").replace(
|
|
"<|endofspeech|>", ""
|
|
)
|
|
if sub_str.startswith("!"):
|
|
sub_str = sub_str[1:]
|
|
if sub_str.startswith("!"): # !!: audio sample point
|
|
sub_str = audio
|
|
try:
|
|
time1 = time.perf_counter()
|
|
data_src = load_audio_text_image_video(sub_str, fs=frontend.fs)
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
except Exception as e:
|
|
logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")
|
|
|
|
speech, speech_lengths = extract_fbank(
|
|
data_src,
|
|
data_type=kwargs.get("data_type", "sound"),
|
|
frontend=frontend,
|
|
is_final=True,
|
|
) # speech: [b, T, d]
|
|
|
|
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
|
|
)
|
|
|
|
if kwargs.get("permute", True):
|
|
speech = speech.permute(0, 2, 1)
|
|
if speech_lengths > kwargs.get("max_source_length", 5500):
|
|
# logging.info(
|
|
# f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}"
|
|
# )
|
|
badcase_flag = True
|
|
|
|
olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
|
|
olens = 1 + (olens - 3 + 2 * 1) // 2
|
|
fake_token_len_i = (olens - 1) // 2 + 1
|
|
fake_token = [0] * fake_token_len_i
|
|
fbank_beg_i = len(source_ids)
|
|
source_ids += fake_token
|
|
fbank_mask_i += [1] * len(fake_token)
|
|
|
|
fbank_beg += [fbank_beg_i + len(input_ids)]
|
|
fake_token_len += [fake_token_len_i]
|
|
source_mask = [-100] * len(source_ids)
|
|
target_out = f"{target_out}<|im_end|>"
|
|
target_ids = tokenizer.encode(target_out)
|
|
input_source_ids = input_ids + source_ids
|
|
input_ids += source_ids + target_ids
|
|
labels += source_mask + target_ids
|
|
fbank_mask += fbank_mask_i
|
|
if len(speech) > 0:
|
|
fbank.append(speech[0, :, :])
|
|
fbank_lens.append(speech_lengths)
|
|
|
|
input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length]
|
|
attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
|
|
labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length]
|
|
|
|
# fbank = speech[0, :, :]
|
|
# fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32)
|
|
fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
|
|
fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)
|
|
fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32)
|
|
source_ids = torch.tensor(input_source_ids, dtype=torch.int64)
|
|
target_ids = torch.tensor(target_ids, dtype=torch.int64)
|
|
|
|
if len(fbank) > 0:
|
|
speech = torch.nn.utils.rnn.pad_sequence(fbank, batch_first=True, padding_value=0.0)
|
|
speech_lengths = torch.nn.utils.rnn.pad_sequence(
|
|
fbank_lens, batch_first=True, padding_value=-1
|
|
)
|
|
else:
|
|
speech = []
|
|
speech_lengths = []
|
|
output = {
|
|
"speech": speech,
|
|
"speech_lengths": speech_lengths,
|
|
"fbank_mask": fbank_mask[None, :],
|
|
"fbank_beg": fbank_beg[None,],
|
|
"fake_token_len": fake_token_len[None, :],
|
|
"input_ids": input_ids[None,],
|
|
"attention_mask": attention_mask[None,],
|
|
"labels_ids": labels,
|
|
"source_ids": source_ids[None, :],
|
|
"target_ids": target_ids[None, :],
|
|
}
|
|
|
|
return output
|
|
|
|
def inference_prepare(
|
|
self,
|
|
data_in,
|
|
data_lengths=None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
frontend=None,
|
|
**kwargs,
|
|
):
|
|
|
|
meta_data = {}
|
|
prompt = kwargs.get("prompt", None)
|
|
|
|
if kwargs.get("batch_size", 1) > 1:
|
|
raise NotImplementedError("batch decoding is not implemented")
|
|
|
|
contents = self.data_template(data_in[0])
|
|
output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs)
|
|
batch = to_device(output, kwargs["device"])
|
|
|
|
# audio encoder
|
|
speech = batch["speech"]
|
|
if len(speech) > 0:
|
|
speech_lengths = batch["speech_lengths"][:, 0]
|
|
# fp16
|
|
if kwargs.get("fp16", False):
|
|
speech = speech.to(torch.float16)
|
|
elif kwargs.get("bf16", False):
|
|
speech = speech.to(torch.bfloat16)
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
# audio_adaptor
|
|
encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)
|
|
|
|
input_ids = batch["input_ids"]
|
|
source_ids = batch["source_ids"]
|
|
fbank_beg = batch["fbank_beg"]
|
|
fake_token_len = batch["fake_token_len"]
|
|
|
|
if not kwargs.get("tearchforing", False):
|
|
input_ids = source_ids
|
|
|
|
input_ids[input_ids < 0] = 0
|
|
inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)
|
|
|
|
batch_size, token_num, dims = inputs_embeds.shape
|
|
|
|
fake_token_len[fake_token_len < 0] = 0
|
|
fbank_beg[fbank_beg < 0] = 0
|
|
|
|
speech_idx = 0
|
|
for batch_idx in range(batch_size):
|
|
|
|
for turn_id in range(fbank_beg.shape[1]):
|
|
fbank_beg_idx = fbank_beg[batch_idx, turn_id].item()
|
|
if fbank_beg_idx > 0:
|
|
speech_token_len = fake_token_len[batch_idx, turn_id]
|
|
speech_token = encoder_out[speech_idx, :speech_token_len, :]
|
|
|
|
try:
|
|
inputs_embeds[
|
|
batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, :
|
|
] = speech_token
|
|
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}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}"
|
|
)
|
|
# import pdb;
|
|
# pdb.set_trace()
|
|
speech_token_len = encoder_out_lens[speech_idx].item()
|
|
speech_token = encoder_out[speech_idx, :speech_token_len, :]
|
|
inputs_embeds[
|
|
batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, :
|
|
] = speech_token
|
|
|
|
speech_idx += 1
|
|
return inputs_embeds, contents, batch, source_ids, meta_data
|
|
|
|
def inference(
|
|
self,
|
|
data_in,
|
|
data_lengths=None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
frontend=None,
|
|
**kwargs,
|
|
):
|
|
|
|
inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare(
|
|
data_in, data_lengths, key, tokenizer, frontend, **kwargs
|
|
)
|
|
|
|
llm_dtype = kwargs.get("llm_dtype", "fp32")
|
|
if llm_dtype == "fp32":
|
|
llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype
|
|
llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype
|
|
|
|
with torch.cuda.amp.autocast(
|
|
enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype]
|
|
):
|
|
label = contents["assistant"][-1]
|
|
self.llm = self.llm.to(dtype_map[llm_dtype])
|
|
inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype])
|
|
|
|
if not kwargs.get("tearchforing", False):
|
|
|
|
generated_ids = self.llm.generate(
|
|
inputs_embeds=inputs_embeds, max_new_tokens=kwargs.get("max_length", 512)
|
|
)
|
|
# generated_ids = [
|
|
# output_ids[len(input_id) :]
|
|
# for input_id, output_ids in zip(input_ids, generated_ids)
|
|
# ]
|
|
response = tokenizer.batch_decode(
|
|
generated_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True)
|
|
)[0]
|
|
|
|
loss = None
|
|
else:
|
|
|
|
labels_ids = batch["labels_ids"]
|
|
labels_ids[labels_ids == -1] = -100
|
|
attention_mask = batch.get("attention_mask", None)
|
|
# attention_mask = attention_mask.to(dtype_map[llm_dtype])
|
|
model_outputs = self.llm(
|
|
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
|
|
)
|
|
|
|
preds = torch.argmax(model_outputs.logits, -1)[:, source_ids.shape[1] :]
|
|
response = tokenizer.batch_decode(
|
|
preds,
|
|
add_special_tokens=False,
|
|
skip_special_tokens=kwargs.get("skip_special_tokens", True),
|
|
)[0]
|
|
loss = model_outputs.loss.item()
|
|
|
|
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 = []
|
|
response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response)
|
|
result_i = {"key": key[0], "text": response, "text_tn": response_clean, "label": label}
|
|
if loss is not None:
|
|
result_i["loss"] = loss
|
|
results.append(result_i)
|
|
|
|
if ibest_writer is not None:
|
|
ibest_writer["text"][key[0]] = response.replace("\n", " ")
|
|
ibest_writer["label"][key[0]] = label.replace("\n", " ")
|
|
ibest_writer["text_tn"][key[0]] = response_clean
|
|
|
|
return results, meta_data
|
|
|
|
|
|
class Swish(torch.nn.Module):
|
|
"""Construct an Swish object."""
|
|
|
|
def forward(self, x):
|
|
"""Return Swich activation function."""
|
|
return x * torch.sigmoid(x)
|
|
|
|
|
|
class LayerNorm(nn.LayerNorm):
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def forward(self, input):
|
|
output = F.layer_norm(
|
|
input.float(),
|
|
self.normalized_shape,
|
|
self.weight.float() if self.weight is not None else None,
|
|
self.bias.float() if self.bias is not None else None,
|
|
self.eps,
|
|
)
|
|
return output.type_as(input)
|
|
|
|
|
|
@tables.register("model_classes", "LLMASR5")
|
|
class LLMASR5(nn.Module):
|
|
""" """
|
|
|
|
def __init__(
|
|
self,
|
|
audio_encoder: str = None,
|
|
audio_encoder_conf: dict = None,
|
|
audio_adaptor: str = None,
|
|
audio_adaptor_conf: dict = None,
|
|
llm: str = None,
|
|
llm_conf: dict = None,
|
|
input_size: int = 80,
|
|
lsm_weight: float = 0.0,
|
|
length_normalized_loss: bool = False,
|
|
audio_decoder: str = None,
|
|
audio_decoder_conf: dict = 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 if hasattr(model.model, "model") else 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)
|
|
freeze_layer_num = int(audio_encoder_conf.get("freeze_layer_num", -1))
|
|
# if freeze_layer_num > 0:
|
|
# freeze_layer_num = range(freeze_layer_num)
|
|
|
|
if freeze:
|
|
for name, param in audio_encoder.named_parameters():
|
|
if freeze_layer_num > 0:
|
|
idx = re.search(r"\.\d+\.", name)
|
|
if idx is not None:
|
|
beg, end = idx.regs[0]
|
|
layer_id = int(name[beg + 1 : end - 1])
|
|
if layer_id < freeze_layer_num:
|
|
param.requires_grad = False
|
|
elif "ln_post." not in name:
|
|
param.requires_grad = False
|
|
else:
|
|
param.requires_grad = False
|
|
|
|
audio_encoder.eval()
|
|
|
|
self.audio_encoder = audio_encoder
|
|
|
|
# llm
|
|
self.llm = None
|
|
|
|
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,
|
|
output_hidden_states=True,
|
|
)
|
|
freeze = llm_conf.get("freeze", True)
|
|
if freeze:
|
|
for name, param in model.named_parameters():
|
|
param.requires_grad = False
|
|
model.eval()
|
|
self.llm_dtype = llm_conf.get("llm_dtype", "fp32")
|
|
self.llm = model.to(dtype_map[self.llm_dtype])
|
|
llm_dim = model.get_input_embeddings().weight.shape[-1]
|
|
|
|
# adaptor
|
|
adaptor_class = tables.adaptor_classes.get(audio_adaptor)
|
|
audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
|
|
audio_adaptor_conf["llm_dim"] = llm_dim
|
|
audio_adaptor = adaptor_class(**audio_adaptor_conf)
|
|
init_param_path = audio_adaptor_conf.get("init_param_path", None)
|
|
if init_param_path is not None:
|
|
src_state = torch.load(init_param_path, map_location="cpu")
|
|
flag = audio_adaptor.load_state_dict(src_state, strict=False)
|
|
logging.info(f"Loading audio_adaptor ckpt: {init_param_path}, status: {flag}")
|
|
|
|
self.audio_adaptor = audio_adaptor
|
|
|
|
self.error_calculator = None
|
|
|
|
self.length_normalized_loss = length_normalized_loss
|
|
self.beam_search = None
|
|
|
|
self.eos = kwargs.get("eos", 151645)
|
|
|
|
# audio decoder related
|
|
|
|
self.codebook_dim = audio_decoder_conf.get("codebook_dim", 1024)
|
|
self.codebook_size = audio_decoder_conf.get("codebook_size", 4096)
|
|
self.lm_out_voc_size = self.codebook_size + 1
|
|
self.audio_decoder = self.build_audio_decoder(name=audio_decoder, conf=audio_decoder_conf)
|
|
self.concat_emb_hidden = audio_decoder_conf.get("concat_emb_hidden", False)
|
|
self.concat_emb_hidden_norm = audio_decoder_conf.get("concat_emb_hidden_norm", False)
|
|
if self.concat_emb_hidden_norm:
|
|
self.hidden_norm = LayerNorm(llm_dim)
|
|
self.fusion_dropout = nn.Dropout(audio_decoder_conf.get("fusion_drop_rate", 0.0))
|
|
self.emb_norm = LayerNorm(llm_dim)
|
|
self.fusion_norm = LayerNorm(self.audio_decoder.embed_unit)
|
|
self.fusion_act = Swish()
|
|
audio_decoder_in_proj_dim = llm_dim * 2 if self.concat_emb_hidden else llm_dim
|
|
self.audio_decoder_in_proj = torch.nn.Linear(
|
|
audio_decoder_in_proj_dim, self.audio_decoder.embed_unit
|
|
)
|
|
self.codec_embedder = torch.nn.Embedding(self.codebook_size, self.codebook_dim)
|
|
self.audio_decoder_embedding = torch.nn.Embedding(2, self.audio_decoder.embed_unit)
|
|
self.ad_sos_eos = 0
|
|
self.ad_task_id = 1
|
|
self.ad_ignore_id = -1
|
|
self.predict_nq = 1
|
|
|
|
from .label_smoothing_loss import LabelSmoothingLoss
|
|
|
|
self.criterion_ce = LabelSmoothingLoss(
|
|
size=self.lm_out_voc_size // self.predict_nq,
|
|
padding_idx=self.ad_ignore_id,
|
|
smoothing=lsm_weight,
|
|
normalize_length=length_normalized_loss,
|
|
reduction=False,
|
|
)
|
|
|
|
mel_decoder_name = kwargs.get("mel_decoder", None)
|
|
mel_decoder_conf = kwargs.get("mel_decoder_conf", None)
|
|
self.mel_decoder = self.build_mel_decoder(name=mel_decoder_name, conf=mel_decoder_conf)
|
|
vocoder_name = kwargs.get("vocoder", None)
|
|
vocoder_conf = kwargs.get("vocoder_conf", None)
|
|
self.vocoder = self.build_vocoder(name=vocoder_name, conf=vocoder_conf)
|
|
|
|
def build_mel_decoder(self, name: str, conf: dict):
|
|
if name is None or conf is None:
|
|
return None
|
|
if name == "MaskedDiffWithXvec":
|
|
from funasr.models.llm_asr.flow_matching import MaskedDiffWithXvec
|
|
return MaskedDiffWithXvec(**conf)
|
|
return None
|
|
|
|
def build_vocoder(self, name: str, conf: dict):
|
|
if name is None or conf is None:
|
|
return None
|
|
if name == "HifiGAN":
|
|
from funasr.models.llm_asr.hifigan import HifiGan
|
|
return HifiGan(**conf)
|
|
return None
|
|
|
|
def build_audio_decoder(self, name, conf):
|
|
if name == "transformer":
|
|
from funasr.models.llm_asr.transformer_lm import TransformerEmbedLM
|
|
|
|
if "text_vocab_size" in conf:
|
|
lm_model = TransformerEmbedLM(vocab_size=self.lm_out_voc_size, **conf)
|
|
else:
|
|
lm_model = TransformerEmbedLM(
|
|
vocab_size=self.lm_out_voc_size, text_vocab_size=self.lm_out_voc_size, **conf
|
|
)
|
|
else:
|
|
raise TypeError(f"Unknown codec decoder type {name}")
|
|
|
|
return lm_model
|
|
|
|
def calc_dense_vector(self, codec, codec_lengths):
|
|
"""
|
|
Args:
|
|
codec: (B, T, Nq)
|
|
codec_lengths: (B, )
|
|
"""
|
|
mask = codec != self.ad_ignore_id
|
|
return self.codec_embedder(codec * mask).sum(dim=-2) * mask
|
|
|
|
def prepare_audio_decoder_io(
|
|
self,
|
|
text: torch.Tensor,
|
|
text_lengths: torch.Tensor,
|
|
codec: Optional[torch.Tensor] = None,
|
|
codec_lengths: Optional[torch.Tensor] = None,
|
|
need_targets: bool = True,
|
|
):
|
|
"""build inputs and targets for language model
|
|
|
|
Normally, this function is called in batchify_nll.
|
|
Args:
|
|
text: (Batch, Length, Dim)
|
|
text_lengths: (Batch,)
|
|
codec: (Batch, Length)
|
|
codec_lengths: (Batch,)
|
|
need_targets: bool, whether provide targets
|
|
"""
|
|
|
|
if need_targets:
|
|
assert (
|
|
codec is not None and codec_lengths is not None
|
|
), "need_target=True, but codec or codec_length is None"
|
|
|
|
sos_eos_emb = self.audio_decoder_embedding(
|
|
torch.tensor([self.ad_sos_eos], dtype=torch.int64, device=text.device)
|
|
)
|
|
task_id_emb = self.audio_decoder_embedding(
|
|
torch.tensor([self.ad_task_id], dtype=torch.int64, device=text.device)
|
|
)
|
|
codec_emb = None
|
|
if codec is not None and codec_lengths is not None:
|
|
codec_emb = self.calc_dense_vector(codec, codec_lengths)
|
|
inputs_list = []
|
|
for i, text_len in enumerate(text_lengths):
|
|
one_input = [sos_eos_emb, text[i, :text_len], task_id_emb]
|
|
if codec_emb is not None:
|
|
one_input.append(codec_emb[i, : codec_lengths[i]])
|
|
inputs_list.append(torch.cat(one_input, dim=0))
|
|
llm_inputs = pad_list(inputs_list, 0.0)
|
|
llm_lengths = text_lengths + 2
|
|
if codec_emb is not None:
|
|
llm_lengths = llm_lengths + codec_lengths
|
|
|
|
if not need_targets:
|
|
return llm_inputs, llm_lengths
|
|
|
|
bb, tt = text.shape[0], codec_lengths.max() + 1
|
|
llm_targets = -1 * torch.ones(
|
|
[bb, tt, self.predict_nq], dtype=torch.int64, device=text.device
|
|
)
|
|
for i, codec_len in enumerate(codec_lengths):
|
|
llm_targets[i, :codec_len] = codec[i, :codec_len]
|
|
llm_targets[i, codec_len] = self.codebook_size + self.ad_sos_eos
|
|
|
|
return (llm_inputs, llm_targets), (llm_lengths, codec_lengths + 1)
|
|
|
|
def nll(
|
|
self,
|
|
text: torch.Tensor,
|
|
text_lengths: torch.Tensor,
|
|
codec: Optional[torch.Tensor] = None,
|
|
codec_lengths: Optional[torch.Tensor] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
|
|
"""Compute negative log likelihood(nll)
|
|
|
|
Normally, this function is called in batchify_nll.
|
|
Args:
|
|
text: (Batch, Length, Dim)
|
|
text_lengths: (Batch,)
|
|
codec: (Batch, Length)
|
|
codec_lengths: (Batch,)
|
|
"""
|
|
batch_size = text.size(0)
|
|
# For data parallel
|
|
text = text[:, : text_lengths.max()]
|
|
codec = codec[:, : codec_lengths.max()]
|
|
# text = self.audio_decoder_in_proj(text)
|
|
|
|
# build inputs and targets for language model
|
|
with autocast(False):
|
|
(sequence, target), (x_lengths, y_lengths) = self.prepare_audio_decoder_io(
|
|
text, text_lengths, codec, codec_lengths, need_targets=True
|
|
)
|
|
|
|
# 2a. Forward Language model
|
|
# x: (Batch, Length) -> y: (Batch, Length, NVocab)
|
|
sequence = sequence[:, : x_lengths.max()]
|
|
target = target[:, : y_lengths.max()]
|
|
y, _ = self.audio_decoder(sequence, x_lengths, text_lengths + 1)
|
|
bb, tt = y.shape[0], y.shape[1]
|
|
y = y.reshape(bb, tt, self.predict_nq, -1)
|
|
# 2b. Extract real logits
|
|
logits_list = []
|
|
for i, (text_len, codec_len) in enumerate(zip(text_lengths, codec_lengths)):
|
|
logits_list.append(y[i, text_len + 1 : text_len + 2 + codec_len])
|
|
logits = pad_list(logits_list, 0.0)
|
|
|
|
# 3. Calc negative log likelihood
|
|
tt = logits.shape[1]
|
|
nll = self.criterion_ce(
|
|
logits.reshape(bb, tt * self.predict_nq, -1), target.reshape(bb, tt * self.predict_nq)
|
|
)
|
|
nll = nll.sum(-1)
|
|
# nll: (BxL,) -> (BxL,)
|
|
nll.masked_fill_(make_pad_mask(y_lengths * self.predict_nq).to(nll.device).view(-1), 0.0)
|
|
# nll: (BxL,) -> (B, L)
|
|
nll = nll.reshape(batch_size, -1).reshape(batch_size, tt, self.predict_nq)
|
|
|
|
return nll, logits, target, codec_lengths + 1
|
|
|
|
def forward(
|
|
self,
|
|
speech: torch.Tensor = None,
|
|
speech_lengths: torch.Tensor = None,
|
|
input_ids: torch.Tensor = None,
|
|
attention_mask: torch.Tensor = None,
|
|
labels_ids: torch.Tensor = None,
|
|
fbank_beg: torch.Tensor = None,
|
|
fbank_mask: torch.Tensor = None,
|
|
**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()
|
|
stats = {}
|
|
input_ids[input_ids < 0] = 0
|
|
inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)
|
|
batch_size, token_num, dims = inputs_embeds.shape
|
|
if speech is not None:
|
|
if len(speech_lengths.size()) > 1:
|
|
speech_lengths = speech_lengths[:, 0]
|
|
|
|
batch_size_speech, frames, _ = speech.shape
|
|
|
|
with torch.cuda.amp.autocast(enabled=False):
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
# audio_adaptor
|
|
encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)
|
|
|
|
fake_token_len = kwargs.get("fake_token_len")
|
|
fake_token_len[fake_token_len < 0] = 0
|
|
fbank_beg[fbank_beg < 0] = 0
|
|
|
|
speech_idx = 0
|
|
for batch_idx in range(batch_size):
|
|
|
|
for turn_id in range(fbank_beg.shape[1]):
|
|
fbank_beg_idx = fbank_beg[batch_idx, turn_id].item()
|
|
if fbank_beg_idx > 0:
|
|
speech_token_len = fake_token_len[batch_idx, turn_id]
|
|
speech_token = encoder_out[speech_idx, :speech_token_len, :]
|
|
|
|
try:
|
|
inputs_embeds[
|
|
batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, :
|
|
] = speech_token
|
|
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}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}"
|
|
)
|
|
# import pdb;
|
|
# pdb.set_trace()
|
|
speech_token_len = encoder_out_lens[speech_idx].item()
|
|
speech_token = encoder_out[speech_idx, :speech_token_len, :]
|
|
inputs_embeds[
|
|
batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, :
|
|
] = speech_token
|
|
|
|
speech_idx += 1
|
|
|
|
stats["batch_size_speech"] = batch_size_speech
|
|
stats["batch_size_x_frames"] = frames * batch_size_speech
|
|
stats["batch_size_real_frames"] = speech_lengths.sum().item()
|
|
stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"]
|
|
|
|
with torch.cuda.amp.autocast(
|
|
enabled=True if self.llm_dtype != "fp32" else False, dtype=dtype_map[self.llm_dtype]
|
|
):
|
|
labels_ids[labels_ids == -1] = -100
|
|
attention_mask[attention_mask < 0] = 0
|
|
model_outputs = self.llm(
|
|
inputs_embeds=inputs_embeds.to(dtype_map[self.llm_dtype]),
|
|
attention_mask=attention_mask,
|
|
labels=labels_ids,
|
|
)
|
|
loss = model_outputs.loss
|
|
|
|
codec = kwargs.get("codec")
|
|
# codec_len = kwargs.get("codec_len")
|
|
# if len(codec_len.size()) > 1:
|
|
# codec_len = codec_len[:, 0]
|
|
codec_len = (codec > 0).sum(-1)
|
|
hidden_states = model_outputs.hidden_states[-1].float()
|
|
|
|
target_ids = []
|
|
target_ids_len = []
|
|
hidden_states_select = []
|
|
for batch_idx in range(labels_ids.shape[0]):
|
|
beg_i = 0
|
|
end_i = 0
|
|
for token_idx in range(labels_ids.shape[1]):
|
|
token_int = labels_ids[batch_idx, token_idx].item()
|
|
if token_int == self.eos:
|
|
target_ids_i = labels_ids[batch_idx, beg_i:end_i]
|
|
target_ids_len_i = end_i - beg_i
|
|
target_ids_len.append(target_ids_len_i)
|
|
target_ids.append(target_ids_i)
|
|
hidden_states_i = hidden_states[batch_idx, beg_i - 1 : end_i - 1, :]
|
|
hidden_states_select.append(hidden_states_i)
|
|
end_i += 1
|
|
beg_i = end_i
|
|
continue
|
|
|
|
end_i += 1
|
|
if token_int <= 0:
|
|
beg_i += 1
|
|
|
|
target_ids = torch.nn.utils.rnn.pad_sequence(
|
|
target_ids, batch_first=True, padding_value=-100
|
|
)
|
|
hidden_states_select = torch.nn.utils.rnn.pad_sequence(
|
|
hidden_states_select, batch_first=True, padding_value=0.0
|
|
)
|
|
target_ids_len = torch.tensor(target_ids_len, dtype=torch.int32, device=input_ids.device)
|
|
target_ids = target_ids.to(device=input_ids.device)
|
|
target_ids[target_ids < 0] = 0
|
|
target_emb = self.llm.model.get_input_embeddings()(target_ids)
|
|
hidden_states_select = hidden_states_select.to(device=input_ids.device)
|
|
if self.concat_emb_hidden:
|
|
if not self.concat_emb_hidden_norm:
|
|
hidden_states_select = torch.concat((hidden_states_select, target_emb), dim=-1)
|
|
hidden_states_select = self.audio_decoder_in_proj(hidden_states_select)
|
|
else:
|
|
outs = self.hidden_norm(hidden_states_select)
|
|
outs = self.fusion_dropout(self.fusion_act(outs))
|
|
# emb = model_outputs.hidden_states[0]
|
|
emb = self.fusion_dropout(self.fusion_act(self.emb_norm(target_emb)))
|
|
outs = self.audio_decoder_in_proj(torch.cat([outs, emb], dim=-1))
|
|
hidden_states_select = self.fusion_act(self.fusion_norm(outs))
|
|
|
|
nll, logits, target, target_lengths = self.nll(
|
|
hidden_states_select, target_ids_len, codec[:, :, None], codec_len
|
|
)
|
|
output_mask = (
|
|
~make_pad_mask(target_lengths, maxlen=target_lengths.max())
|
|
.to(hidden_states_select.device)
|
|
.unsqueeze(-1)
|
|
)
|
|
total, batch_size = output_mask.sum() * self.predict_nq, nll.shape[0] * self.predict_nq
|
|
denom = total if self.length_normalized_loss else batch_size
|
|
loss = (nll * output_mask).sum() / denom
|
|
|
|
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
|
|
|
|
cc = logits.shape[-1]
|
|
for i in range(self.predict_nq):
|
|
acc = th_accuracy(
|
|
logits[:, :, i, :].reshape(-1, cc), target[:, :, i], self.ad_ignore_id
|
|
)
|
|
stats[f"codec_acc_{i + 1}"] = acc
|
|
|
|
stats["loss"] = torch.clone(loss.detach())
|
|
stats["batch_size"] = batch_size
|
|
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"]
|
|
|
|
dialog_turns = (fbank_beg > 0).sum(-1)
|
|
dialog_turns_max = torch.max(dialog_turns).int().item()
|
|
dialog_turns_avg = dialog_turns.sum().item() / batch_size
|
|
stats["dialog_turns_max"] = dialog_turns_max
|
|
stats["dialog_turns_avg"] = dialog_turns_avg
|
|
|
|
# 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 encode(self, speech, speech_lengths):
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)
|
|
|
|
return encoder_out, encoder_out_lens
|
|
|
|
def data_template(self, data):
|
|
system, user, assistant = [], [], []
|
|
for i, item in enumerate(data):
|
|
role = item["role"]
|
|
content = item["content"]
|
|
if role == "system":
|
|
system.append(content)
|
|
elif role == "user":
|
|
user.append(content)
|
|
elif role == "assistant":
|
|
assistant.append(content)
|
|
|
|
system = system * len(user)
|
|
|
|
contents = {
|
|
"system": system,
|
|
"user": user,
|
|
"assistant": assistant,
|
|
}
|
|
|
|
return contents
|
|
|
|
def data_load_speech(self, contents: dict, tokenizer, frontend, meta_data={}, **kwargs):
|
|
|
|
system = contents["system"]
|
|
user = contents["user"]
|
|
assistant = contents["assistant"]
|
|
pattern = re.compile(r"(<\|startofspeech\|>.*?<\|endofspeech\|>)")
|
|
|
|
input_ids, labels, fbank, fbank_lens, fbank_mask, fbank_beg, fake_token_len = (
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
[],
|
|
)
|
|
input_source_ids = []
|
|
for i, (system_prompt, user_prompt, target_out) in enumerate(zip(system, user, assistant)):
|
|
if i >= kwargs.get("multiturn_num_max", 5):
|
|
break
|
|
if len(input_ids) > kwargs.get("max_token_length", 1500):
|
|
break
|
|
|
|
if i == 0:
|
|
source_input = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
|
|
else:
|
|
source_input = f"<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
|
|
|
|
splits = pattern.split(source_input)
|
|
source_ids = []
|
|
fbank_i = []
|
|
fbank_mask_i = []
|
|
fake_token_len_i = 0
|
|
fbank_beg_i = -1
|
|
fbank_lens_i = []
|
|
speech, speech_lengths = [], []
|
|
for k, sub_str in enumerate(splits):
|
|
if not sub_str.startswith("<|startofspeech|>"):
|
|
sub_token = tokenizer.encode(sub_str)
|
|
source_ids += sub_token
|
|
fbank_mask_i += [0] * len(sub_token)
|
|
else:
|
|
sub_str = sub_str.replace("<|startofspeech|>", "").replace(
|
|
"<|endofspeech|>", ""
|
|
)
|
|
if sub_str.startswith("!"):
|
|
sub_str = sub_str[1:]
|
|
if sub_str.startswith("!"): # !!bytes
|
|
sub_str = eval(sub_str[1:])
|
|
try:
|
|
time1 = time.perf_counter()
|
|
data_src = load_audio_text_image_video(sub_str, fs=frontend.fs)
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
except Exception as e:
|
|
logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")
|
|
|
|
speech, speech_lengths = extract_fbank(
|
|
data_src,
|
|
data_type=kwargs.get("data_type", "sound"),
|
|
frontend=frontend,
|
|
is_final=True,
|
|
) # speech: [b, T, d]
|
|
|
|
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
|
|
)
|
|
|
|
if kwargs.get("permute", True):
|
|
speech = speech.permute(0, 2, 1)
|
|
if speech_lengths > kwargs.get("max_source_length", 5500):
|
|
# logging.info(
|
|
# f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}"
|
|
# )
|
|
badcase_flag = True
|
|
|
|
olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
|
|
olens = 1 + (olens - 3 + 2 * 1) // 2
|
|
fake_token_len_i = (olens - 1) // 2 + 1
|
|
fake_token = [0] * fake_token_len_i
|
|
fbank_beg_i = len(source_ids)
|
|
source_ids += fake_token
|
|
fbank_mask_i += [1] * len(fake_token)
|
|
|
|
fbank_beg += [fbank_beg_i + len(input_ids)]
|
|
fake_token_len += [fake_token_len_i]
|
|
source_mask = [-100] * len(source_ids)
|
|
splits = pattern.split(target_out)
|
|
for k, sub_str in enumerate(splits):
|
|
if len(sub_str) < 1:
|
|
continue
|
|
if not sub_str.startswith("<|startofspeech|>"):
|
|
sub_str = f"{sub_str}<|im_end|>"
|
|
sub_token = tokenizer.encode(sub_str)
|
|
target_ids = sub_token
|
|
# target_out = f"{target_out}<|im_end|>"
|
|
# target_ids = tokenizer.encode(target_out)
|
|
input_source_ids = input_ids + source_ids
|
|
input_ids += source_ids + target_ids
|
|
labels += source_mask + target_ids
|
|
fbank_mask += fbank_mask_i
|
|
if len(speech) > 0:
|
|
fbank.append(speech[0, :, :])
|
|
fbank_lens.append(speech_lengths)
|
|
|
|
input_ids = torch.tensor(input_ids, dtype=torch.int64) # [: self.max_token_length]
|
|
attention_mask = torch.tensor([1] * len(input_ids), dtype=torch.int32)
|
|
labels = torch.tensor(labels, dtype=torch.int64) # [: self.max_token_length]
|
|
|
|
# fbank = speech[0, :, :]
|
|
# fbank_lens = torch.tensor(fbank_lens, dtype=torch.int32)
|
|
fbank_mask = torch.tensor(fbank_mask, dtype=torch.float32)
|
|
fbank_beg = torch.tensor(fbank_beg, dtype=torch.int32)
|
|
fake_token_len = torch.tensor(fake_token_len, dtype=torch.int32)
|
|
source_ids = torch.tensor(input_source_ids, dtype=torch.int64)
|
|
target_ids = torch.tensor(target_ids, dtype=torch.int64)
|
|
|
|
if len(fbank) > 0:
|
|
speech = torch.nn.utils.rnn.pad_sequence(fbank, batch_first=True, padding_value=0.0)
|
|
speech_lengths = torch.nn.utils.rnn.pad_sequence(
|
|
fbank_lens, batch_first=True, padding_value=-1
|
|
)
|
|
else:
|
|
speech = []
|
|
speech_lengths = []
|
|
output = {
|
|
"speech": speech,
|
|
"speech_lengths": speech_lengths,
|
|
"fbank_mask": fbank_mask[None, :],
|
|
"fbank_beg": fbank_beg[None,],
|
|
"fake_token_len": fake_token_len[None, :],
|
|
"input_ids": input_ids[None,],
|
|
"attention_mask": attention_mask[None,],
|
|
"labels_ids": labels,
|
|
"source_ids": source_ids[None, :],
|
|
"target_ids": target_ids[None, :],
|
|
}
|
|
|
|
return output
|
|
|
|
def inference_prepare(
|
|
self,
|
|
data_in,
|
|
data_lengths=None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
frontend=None,
|
|
**kwargs,
|
|
):
|
|
|
|
meta_data = {}
|
|
prompt = kwargs.get("prompt", None)
|
|
|
|
if kwargs.get("batch_size", 1) > 1:
|
|
raise NotImplementedError("batch decoding is not implemented")
|
|
|
|
contents = self.data_template(data_in[0])
|
|
output = self.data_load_speech(contents, tokenizer, frontend, meta_data=meta_data, **kwargs)
|
|
batch = to_device(output, kwargs["device"])
|
|
|
|
# audio encoder
|
|
speech = batch["speech"]
|
|
if len(speech) > 0:
|
|
speech_lengths = batch["speech_lengths"][:, 0]
|
|
# fp16
|
|
if kwargs.get("fp16", False):
|
|
speech = speech.to(torch.float16)
|
|
elif kwargs.get("bf16", False):
|
|
speech = speech.to(torch.bfloat16)
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
# audio_adaptor
|
|
encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)
|
|
|
|
input_ids = batch["input_ids"]
|
|
source_ids = batch["source_ids"]
|
|
fbank_beg = batch["fbank_beg"]
|
|
fake_token_len = batch["fake_token_len"]
|
|
|
|
if not kwargs.get("tearchforing", False):
|
|
input_ids = source_ids
|
|
|
|
input_ids[input_ids < 0] = 0
|
|
inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)
|
|
|
|
batch_size, token_num, dims = inputs_embeds.shape
|
|
|
|
fake_token_len[fake_token_len < 0] = 0
|
|
fbank_beg[fbank_beg < 0] = 0
|
|
|
|
speech_idx = 0
|
|
for batch_idx in range(batch_size):
|
|
|
|
for turn_id in range(fbank_beg.shape[1]):
|
|
fbank_beg_idx = fbank_beg[batch_idx, turn_id].item()
|
|
if fbank_beg_idx > 0:
|
|
speech_token_len = fake_token_len[batch_idx, turn_id]
|
|
speech_token = encoder_out[speech_idx, :speech_token_len, :]
|
|
|
|
try:
|
|
inputs_embeds[
|
|
batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, :
|
|
] = speech_token
|
|
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}, speech_token_len: {speech_token_len}, encoder_out: {encoder_out.shape}, encoder_out_lens: {encoder_out_lens}, fake_token_len: {fake_token_len}, speech_lengths: {speech_lengths}"
|
|
)
|
|
# import pdb;
|
|
# pdb.set_trace()
|
|
speech_token_len = encoder_out_lens[speech_idx].item()
|
|
speech_token = encoder_out[speech_idx, :speech_token_len, :]
|
|
inputs_embeds[
|
|
batch_idx, fbank_beg_idx : fbank_beg_idx + speech_token_len, :
|
|
] = speech_token
|
|
|
|
speech_idx += 1
|
|
return inputs_embeds, contents, batch, source_ids, meta_data
|
|
|
|
def inference(
|
|
self,
|
|
data_in,
|
|
data_lengths=None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
frontend=None,
|
|
**kwargs,
|
|
):
|
|
|
|
inputs_embeds, contents, batch, source_ids, meta_data = self.inference_prepare(
|
|
data_in, data_lengths, key, tokenizer, frontend, **kwargs
|
|
)
|
|
|
|
llm_dtype = kwargs.get("llm_dtype", "fp32")
|
|
if llm_dtype == "fp32":
|
|
llm_dtype = "fp16" if kwargs.get("fp16", False) else llm_dtype
|
|
llm_dtype = "bf16" if kwargs.get("bf16", False) else llm_dtype
|
|
|
|
with torch.cuda.amp.autocast(
|
|
enabled=True if llm_dtype != "fp32" else False, dtype=dtype_map[llm_dtype]
|
|
):
|
|
label = contents["assistant"][-1]
|
|
self.llm = self.llm.to(dtype_map[llm_dtype])
|
|
inputs_embeds = inputs_embeds.to(dtype_map[llm_dtype])
|
|
|
|
# set random seed for reproduce
|
|
set_all_random_seed(0)
|
|
generated_ids = self.llm.generate(
|
|
inputs_embeds=inputs_embeds,
|
|
max_new_tokens=kwargs.get("max_length", 512),
|
|
output_hidden_states=True,
|
|
return_dict_in_generate=True,
|
|
output_scores=True,
|
|
)
|
|
hidden_states = generated_ids[
|
|
"hidden_states"
|
|
] # hidden_states: (t1, t2, ..., tn, ..., tN), tn=(l1, l2, ..., ln, ..., lN), ln: shape: 1x1x3584
|
|
|
|
token_num = len(hidden_states)
|
|
hidden_states_select = torch.zeros((1, token_num, 3584), dtype=torch.float32).to(
|
|
inputs_embeds.device
|
|
)
|
|
hidden_states_out_len = torch.tensor(
|
|
[
|
|
token_num,
|
|
],
|
|
dtype=torch.int32,
|
|
).to(inputs_embeds.device)
|
|
for i in range(token_num):
|
|
hidden_states_select[0, i, :] = hidden_states[i][-1][0, 0, :].to(torch.float32)
|
|
|
|
target_ids = generated_ids["sequences"]
|
|
target_emb = self.llm.model.get_input_embeddings()(target_ids)
|
|
if self.concat_emb_hidden:
|
|
if not self.concat_emb_hidden_norm:
|
|
hidden_states_select = torch.concat((hidden_states_select, target_emb), dim=-1)
|
|
hidden_states_select = self.audio_decoder_in_proj(hidden_states_select)
|
|
else:
|
|
outs = self.hidden_norm(hidden_states_select)
|
|
outs = self.fusion_dropout(self.fusion_act(outs))
|
|
# emb = model_outputs.hidden_states[0]
|
|
emb = self.fusion_dropout(self.fusion_act(self.emb_norm(target_emb)))
|
|
outs = self.audio_decoder_in_proj(torch.cat([outs, emb], dim=-1))
|
|
hidden_states_select = self.fusion_act(self.fusion_norm(outs))
|
|
|
|
# set random seed for reproduce
|
|
set_all_random_seed(0)
|
|
speech_tokens = self.audio_decode(hidden_states_select, hidden_states_out_len)[
|
|
:, :, 0
|
|
] # 1xlx1: 2,10,1023
|
|
|
|
# generated_ids = [
|
|
# output_ids[len(input_id) :]
|
|
# for input_id, output_ids in zip(input_ids, generated_ids)
|
|
# ]
|
|
response = tokenizer.batch_decode(
|
|
target_ids, skip_special_tokens=kwargs.get("skip_special_tokens", True)
|
|
)[0]
|
|
|
|
loss = None
|
|
|
|
# synthesize waveforms
|
|
spk_emb = kwargs.get("spk_emb", None)
|
|
feat, wav = self.synthesize_waveform(speech_tokens, spk_emb, inputs_embeds.device)
|
|
|
|
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"]
|
|
|
|
self.write_mel_wav(kwargs.get("output_dir"), feat, wav, key[0])
|
|
|
|
results = []
|
|
response_clean = re.sub(r"[^\w\s\u3000\u4e00-\u9fff]+", "", response)
|
|
result_i = {
|
|
"key": key[0],
|
|
"text": response,
|
|
"text_tn": response_clean,
|
|
"label": label,
|
|
"speech_tokens": speech_tokens,
|
|
}
|
|
if loss is not None:
|
|
result_i["loss"] = loss
|
|
results.append(result_i)
|
|
|
|
speech_tokens_out = "<|startofspeech|>"
|
|
for i in range(speech_tokens.shape[-1]):
|
|
tmp = speech_tokens[0, i].item()
|
|
speech_tokens_out += f"<|c{tmp}|>"
|
|
speech_tokens_out += "<|endofspeech|><|im_end|>"
|
|
if ibest_writer is not None:
|
|
ibest_writer["text"][key[0]] = response.replace("\n", " ")
|
|
ibest_writer["label"][key[0]] = label.replace("\n", " ")
|
|
ibest_writer["text_tn"][key[0]] = response_clean
|
|
ibest_writer["speech_tokens"][key[0]] = speech_tokens_out
|
|
|
|
return results, meta_data
|
|
|
|
def write_mel_wav(self, output_dir, feat, wav, key):
|
|
out_dir = os.path.join(output_dir, "1best_recog", "mels")
|
|
os.makedirs(out_dir, exist_ok=True)
|
|
if feat is not None:
|
|
feat = feat.cpu().numpy()[0]
|
|
np.save(os.path.join(out_dir, f"{key}.npy"), feat)
|
|
|
|
out_dir = os.path.join(output_dir, "1best_recog", "wavs")
|
|
os.makedirs(out_dir, exist_ok=True)
|
|
if wav is not None:
|
|
path = os.path.join(out_dir, f"{key}.wav")
|
|
torchaudio.save(
|
|
path, wav.cpu(), sample_rate=self.vocoder.sample_rate,
|
|
encoding='PCM_S', bits_per_sample=16
|
|
)
|
|
|
|
def synthesize_waveform(self, speech_tokens, spk_emb, device):
|
|
mel_feat, wav = None, None
|
|
if self.mel_decoder is not None and spk_emb is not None:
|
|
# mel_feat in BxCxT
|
|
mel_feat = self.token2mel(speech_tokens, spk_emb, device)
|
|
if self.vocoder is not None:
|
|
wav = self.vocoder.inference(mel_feat.transpose(1, 2))
|
|
|
|
return mel_feat, wav
|
|
|
|
def token2mel(self, tokens: torch.Tensor, xvec: torch.Tensor, device: torch.device):
|
|
xvec = torch.tensor(xvec).to(device).unsqueeze(0)
|
|
xvec_lens = torch.tensor([xvec.shape[1]], device=device, dtype=torch.int64)
|
|
token_lens = torch.tensor([tokens.shape[1]], device=device, dtype=torch.int64)
|
|
feat = self.mel_decoder.inference(
|
|
tokens, token_lens,
|
|
xvec, xvec_lens,
|
|
diff_steps=10,
|
|
temperature=1.0,
|
|
prompt=dict(
|
|
prompt_text=(None, None),
|
|
prompt_audio=(None, None)
|
|
)
|
|
)
|
|
return feat
|
|
|
|
def audio_decode(
|
|
self,
|
|
text: torch.Tensor,
|
|
text_lengths: torch.Tensor,
|
|
min_length=None,
|
|
max_length: int = 30 * 25,
|
|
infer_cfg_ratio=None,
|
|
decoding_length=None,
|
|
):
|
|
# 1. encode text
|
|
# text = self.audio_decoder_in_proj(text)
|
|
device = text.device
|
|
sos_eos_emb = self.audio_decoder_embedding(
|
|
torch.tensor([[self.ad_sos_eos]], dtype=torch.int64, device=device)
|
|
)
|
|
task_id_emb = self.audio_decoder_embedding(
|
|
torch.tensor([[self.ad_task_id]], dtype=torch.int64, device=device)
|
|
)
|
|
prompt = torch.cat([sos_eos_emb, text, task_id_emb], dim=1)
|
|
seq_input = torch.zeros(
|
|
[1, prompt.shape[1] + max_length, prompt.shape[2]],
|
|
dtype=torch.float32, device=device
|
|
)
|
|
seq_input[:, :prompt.shape[1], :] = prompt
|
|
out_tokens = torch.zeros([1, max_length, 1], dtype=torch.int64, device=device)
|
|
out_token_len = 0
|
|
prompt_len = prompt.shape[1]
|
|
state, hit_eos = None, False
|
|
for i in range(max_length):
|
|
# use state for speedup
|
|
pred, (state, _) = self.audio_decoder.score(seq_input[0, :prompt_len+out_token_len], state, prompt[0])
|
|
|
|
# sampling all `nq` token ids
|
|
pred = pred.reshape(self.predict_nq, -1)
|
|
# normalize scores
|
|
pred = torch.log_softmax(pred, dim=-1)
|
|
if min_length is not None and i < min_length:
|
|
pred[:, self.codebook_size + self.ad_sos_eos] = float(np.finfo(np.float32).min)
|
|
top_ids = self.ras_sampling(pred[0], out_tokens[0, :out_token_len, 0])
|
|
|
|
if torch.any(top_ids == (self.codebook_size + self.ad_sos_eos)):
|
|
hit_eos = True
|
|
out_tokens = out_tokens[:, :out_token_len, :]
|
|
break
|
|
|
|
out_tokens[0, out_token_len, 0] = top_ids[0]
|
|
seq_input[0, prompt_len + out_token_len, :] = self.codec_embedder(top_ids)[0]
|
|
out_token_len += 1
|
|
|
|
if decoding_length is None:
|
|
return out_tokens
|
|
else:
|
|
return out_tokens, hit_eos
|
|
|
|
# Repetition Aware Sampling in VALL-E 2
|
|
def ras_sampling(
|
|
self, weighted_scores, decoded_tokens, *, top_p=0.8, top_k=25, win_size=10, tau_r=0.1
|
|
):
|
|
top_ids = self.nucleus_sampling(weighted_scores, top_p=top_p, top_k=top_k)
|
|
rep_num = torch.sum(decoded_tokens[-win_size:] == top_ids).item()
|
|
if rep_num >= win_size * tau_r:
|
|
top_ids = self.random_sampling(weighted_scores)
|
|
|
|
return top_ids
|
|
|
|
def nucleus_sampling(self, weighted_scores, top_p=0.8, top_k=25):
|
|
cum_prob = 0.0
|
|
sorted_value, sorted_idx = weighted_scores.softmax(dim=0).sort(descending=True, stable=True)
|
|
i = len(sorted_idx)
|
|
for i in range(len(sorted_idx)):
|
|
# sampling both top-p and numbers.
|
|
if cum_prob < top_p and i < top_k:
|
|
cum_prob += sorted_value[i]
|
|
else:
|
|
break
|
|
prob = sorted_value[:i]
|
|
indices = sorted_idx[:i]
|
|
sampling_ids = prob.multinomial(1, replacement=True)
|
|
top_ids = indices[sampling_ids]
|
|
return top_ids
|
|
|
|
def random_sampling(self, weighted_scores):
|
|
top_ids = weighted_scores.softmax(dim=0).multinomial(1, replacement=True)
|
|
return top_ids
|