mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
746 lines
27 KiB
Python
746 lines
27 KiB
Python
import logging
|
|
from typing import Union, Dict, List, Tuple, Optional
|
|
|
|
import time
|
|
import torch
|
|
import torch.nn as nn
|
|
import torch.nn.functional as F
|
|
from torch.cuda.amp import autocast
|
|
import re
|
|
from funasr.models.scama.utils import sequence_mask
|
|
from funasr.losses.label_smoothing_loss import LabelSmoothingLoss
|
|
from funasr.models.ctc.ctc import CTC
|
|
from funasr.models.transformer.utils.add_sos_eos import add_sos_eos
|
|
from funasr.metrics.compute_acc import th_accuracy, compute_accuracy
|
|
from funasr.metrics.common import ErrorCalculator
|
|
from funasr.train_utils.device_funcs import force_gatherable
|
|
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
|
from funasr.utils import postprocess_utils
|
|
from funasr.utils.datadir_writer import DatadirWriter
|
|
from funasr.register import tables
|
|
from funasr.train_utils.device_funcs import to_device
|
|
import traceback
|
|
|
|
|
|
@tables.register("model_classes", "LLMASR")
|
|
class LLMASR(nn.Module):
|
|
""" """
|
|
|
|
def __init__(
|
|
self,
|
|
specaug: str = None,
|
|
specaug_conf: dict = None,
|
|
normalize: str = None,
|
|
normalize_conf: dict = None,
|
|
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__()
|
|
|
|
if specaug is not None:
|
|
specaug_class = tables.specaug_classes.get(specaug)
|
|
specaug = specaug_class(**specaug_conf)
|
|
if normalize is not None:
|
|
normalize_class = tables.normalize_classes.get(normalize)
|
|
normalize = normalize_class(**normalize_conf)
|
|
|
|
# audio encoder
|
|
hub = audio_encoder_conf.get("hub", None)
|
|
if hub == "ms":
|
|
from funasr import AutoModel
|
|
|
|
model = AutoModel(model=audio_encoder, model_revision="master")
|
|
# frontend = model.kwargs.get("frontend")
|
|
audio_encoder_output_size = model.model.encoder_output_size
|
|
|
|
audio_encoder = model.model.model.encoder
|
|
|
|
# self.frontend = frontend
|
|
|
|
elif hub == "hf":
|
|
pass
|
|
else:
|
|
encoder_class = tables.encoder_classes.get(audio_encoder)
|
|
audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf)
|
|
audio_encoder_output_size = audio_encoder.output_size()
|
|
freeze = audio_encoder_conf.get("freeze", True)
|
|
if freeze:
|
|
for name, param in audio_encoder.named_parameters():
|
|
param.requires_grad = False
|
|
audio_encoder.eval()
|
|
|
|
self.audio_encoder = audio_encoder
|
|
|
|
# llm
|
|
hub = llm_conf.get("hub", "hf")
|
|
self.llm = None
|
|
if hub == "hf":
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
|
|
|
|
init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
init_param_path,
|
|
load_in_8bit=None,
|
|
device_map=None,
|
|
use_cache=None,
|
|
)
|
|
freeze = llm_conf.get("freeze", True)
|
|
if freeze:
|
|
for name, param in model.named_parameters():
|
|
param.requires_grad = False
|
|
model.eval()
|
|
self.llm = model
|
|
|
|
# adaptor
|
|
adaptor_class = tables.adaptor_classes.get(audio_adaptor)
|
|
audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
|
|
audio_adaptor = adaptor_class(**audio_adaptor_conf)
|
|
|
|
self.audio_adaptor = audio_adaptor
|
|
|
|
self.blank_id = blank_id
|
|
self.sos = sos if sos is not None else vocab_size - 1
|
|
self.eos = eos if eos is not None else vocab_size - 1
|
|
self.vocab_size = vocab_size
|
|
self.ignore_id = ignore_id
|
|
self.specaug = specaug
|
|
self.normalize = normalize
|
|
|
|
self.criterion_att = LabelSmoothingLoss(
|
|
size=vocab_size,
|
|
padding_idx=ignore_id,
|
|
smoothing=lsm_weight,
|
|
normalize_length=length_normalized_loss,
|
|
)
|
|
|
|
self.error_calculator = None
|
|
|
|
self.length_normalized_loss = length_normalized_loss
|
|
self.beam_search = None
|
|
|
|
def forward(
|
|
self,
|
|
speech: torch.Tensor,
|
|
speech_lengths: torch.Tensor,
|
|
text: torch.Tensor,
|
|
text_lengths: torch.Tensor,
|
|
input_ids: torch.Tensor,
|
|
attention_mask: torch.Tensor,
|
|
labels_ids: torch.Tensor,
|
|
label_mask: torch.Tensor,
|
|
audio_mask: torch.Tensor,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
|
|
"""Encoder + Decoder + Calc loss
|
|
Args:
|
|
speech: (Batch, Length, ...)
|
|
speech_lengths: (Batch, )
|
|
text: (Batch, Length)
|
|
text_lengths: (Batch,)
|
|
"""
|
|
# import pdb;
|
|
# pdb.set_trace()
|
|
if len(text_lengths.size()) > 1:
|
|
text_lengths = text_lengths[:, 0]
|
|
if len(speech_lengths.size()) > 1:
|
|
speech_lengths = speech_lengths[:, 0]
|
|
|
|
batch_size = speech.shape[0]
|
|
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
# audio_adaptor
|
|
encoder_out = self.audio_adaptor(encoder_out)
|
|
|
|
input_ids[input_ids == -1] = 0
|
|
input_ids[input_ids == -100] = 0
|
|
if hasattr(self.llm.model, "embed_tokens"):
|
|
inputs_embeds = self.llm.model.embed_tokens(input_ids)
|
|
elif hasattr(self.llm.model.model, "embed_tokens"):
|
|
inputs_embeds = self.llm.model.model.embed_tokens(input_ids)
|
|
else:
|
|
inputs_embeds = self.llm.model.model.model.embed_tokens(input_ids)
|
|
|
|
if audio_mask is not None:
|
|
batch_size, token_num, dims = inputs_embeds.shape
|
|
_, l, _ = encoder_out.shape
|
|
# [audio, bos, prompt, input, pad]
|
|
encoder_outs_pad = F.pad(encoder_out, (0, 0, 0, token_num - l, 0, 0), value=0.0)
|
|
inputs_embeds = encoder_outs_pad * audio_mask[:, :, None] + inputs_embeds * (
|
|
1.0 - audio_mask[:, :, None]
|
|
)
|
|
|
|
model_outputs = self.llm(
|
|
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
|
|
)
|
|
loss = model_outputs.loss
|
|
|
|
stats = {}
|
|
with torch.no_grad():
|
|
preds = torch.argmax(model_outputs.logits, -1)
|
|
acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
|
|
stats["acc"] = acc_att
|
|
|
|
stats["loss"] = torch.clone(loss.detach())
|
|
|
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
|
if self.length_normalized_loss:
|
|
batch_size = int((text_lengths + 1).sum())
|
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
|
return loss, stats, weight
|
|
|
|
def encode(
|
|
self,
|
|
speech: torch.Tensor,
|
|
speech_lengths: torch.Tensor,
|
|
**kwargs,
|
|
):
|
|
speech = speech.permute(0, 2, 1)
|
|
res = self.audio_encoder(speech)
|
|
if isinstance(res, (list, tuple)):
|
|
encoder_out, encoder_out_lens = res[0], res[1]
|
|
else:
|
|
encoder_out, encoder_out_lens = res, speech_lengths
|
|
return encoder_out, encoder_out_lens
|
|
|
|
def inference(
|
|
self,
|
|
data_in,
|
|
data_lengths=None,
|
|
key: list = None,
|
|
tokenizer=None,
|
|
frontend=None,
|
|
**kwargs,
|
|
):
|
|
|
|
prompt = kwargs.get("prompt", "Transcribe speech to text.")
|
|
|
|
if kwargs.get("batch_size", 1) > 1:
|
|
raise NotImplementedError("batch decoding is not implemented")
|
|
|
|
meta_data = {}
|
|
if (
|
|
isinstance(data_in, torch.Tensor) and kwargs.get("data_type", "sound") == "fbank"
|
|
): # fbank
|
|
speech, speech_lengths = data_in, data_lengths
|
|
if len(speech.shape) < 3:
|
|
speech = speech[None, :, :]
|
|
if speech_lengths is None:
|
|
speech_lengths = speech.shape[1]
|
|
else:
|
|
# extract fbank feats
|
|
time1 = time.perf_counter()
|
|
audio_sample_list = load_audio_text_image_video(
|
|
data_in,
|
|
fs=frontend.fs,
|
|
audio_fs=kwargs.get("fs", 16000),
|
|
data_type=kwargs.get("data_type", "sound"),
|
|
tokenizer=tokenizer,
|
|
)
|
|
time2 = time.perf_counter()
|
|
meta_data["load_data"] = f"{time2 - time1:0.3f}"
|
|
speech, speech_lengths = extract_fbank(
|
|
audio_sample_list, data_type=kwargs.get("data_type", "sound"), frontend=frontend
|
|
)
|
|
time3 = time.perf_counter()
|
|
meta_data["extract_feat"] = f"{time3 - time2:0.3f}"
|
|
meta_data["batch_data_time"] = (
|
|
speech_lengths.sum().item() * frontend.frame_shift * frontend.lfr_n / 1000
|
|
)
|
|
|
|
speech = speech.to(device=kwargs["device"])
|
|
speech_lengths = speech_lengths.to(device=kwargs["device"])
|
|
|
|
# Encoder
|
|
encoder_out, encoder_out_lens = self.encode(speech, speech_lengths)
|
|
|
|
# adaptor
|
|
encoder_out = self.audio_adaptor(encoder_out)
|
|
|
|
prompt_pre = "USER: \nINSTRUCTION: {}\nINPUT: ".format(prompt)
|
|
prompt_ids = tokenizer.encode(prompt_pre)
|
|
prompt_length = len(prompt_ids)
|
|
prompt_ids = torch.tensor(prompt_ids, dtype=torch.int64).to(kwargs["device"])
|
|
|
|
if hasattr(self.llm.model, "embed_tokens"):
|
|
inputs_embeds = self.llm.model.embed_tokens(prompt_ids)
|
|
elif hasattr(self.llm.model.model, "embed_tokens"):
|
|
inputs_embeds = self.llm.model.model.embed_tokens(prompt_ids)
|
|
else:
|
|
inputs_embeds = self.llm.model.model.model.embed_tokens(prompt_ids)
|
|
|
|
inputs_embeds = torch.cat(
|
|
(inputs_embeds[None, :, :], encoder_out), dim=1
|
|
) # [prompt, audio]
|
|
attention_mask = torch.ones(inputs_embeds.size()[:-1], dtype=torch.long).to(
|
|
kwargs["device"]
|
|
)
|
|
|
|
preds = self.llm.generate(
|
|
inputs_embeds=inputs_embeds,
|
|
max_length=kwargs.get("max_length", 200),
|
|
max_new_tokens=kwargs.get("max_new_tokens", 200),
|
|
num_beams=kwargs.get("num_beams", 4),
|
|
do_sample=kwargs.get("do_sample", False),
|
|
min_length=kwargs.get("min_length", 1),
|
|
top_p=kwargs.get("top_p", 1.0),
|
|
repetition_penalty=kwargs.get("repetition_penalty", 1.0),
|
|
length_penalty=kwargs.get("length_penalty", 1.0),
|
|
temperature=kwargs.get("temperature", 1.0),
|
|
attention_mask=attention_mask,
|
|
bos_token_id=tokenizer.bos_token_id,
|
|
eos_token_id=tokenizer.eos_token_id,
|
|
pad_token_id=tokenizer.pad_token_id,
|
|
)
|
|
|
|
text = tokenizer.batch_decode(preds, add_special_tokens=False, skip_special_tokens=True)
|
|
|
|
text = text[0].split(": ")[-1]
|
|
text = text.strip()
|
|
|
|
# preds = torch.argmax(model_outputs.logits, -1)
|
|
|
|
ibest_writer = None
|
|
if kwargs.get("output_dir") is not None:
|
|
if not hasattr(self, "writer"):
|
|
self.writer = DatadirWriter(kwargs.get("output_dir"))
|
|
ibest_writer = self.writer[f"{0 + 1}best_recog"]
|
|
|
|
results = []
|
|
result_i = {"key": key[0], "text": text}
|
|
results.append(result_i)
|
|
|
|
if ibest_writer is not None:
|
|
ibest_writer["text"][key[0]] = text
|
|
|
|
return results, meta_data
|
|
|
|
|
|
@tables.register("model_classes", "LLMASR2")
|
|
class LLMASR2(nn.Module):
|
|
""" """
|
|
|
|
def __init__(
|
|
self,
|
|
specaug: str = None,
|
|
specaug_conf: dict = None,
|
|
normalize: str = None,
|
|
normalize_conf: dict = None,
|
|
audio_encoder: str = None,
|
|
audio_encoder_conf: dict = None,
|
|
audio_adaptor: str = None,
|
|
audio_adaptor_conf: dict = None,
|
|
decoder: str = None,
|
|
decoder_conf: dict = None,
|
|
ctc: str = None,
|
|
ctc_conf: dict = None,
|
|
ctc_weight: float = 0.5,
|
|
llm: str = None,
|
|
llm_conf: dict = None,
|
|
input_size: int = 80,
|
|
vocab_size: int = -1,
|
|
ignore_id: int = -1,
|
|
blank_id: int = 0,
|
|
sos: int = 1,
|
|
eos: int = 2,
|
|
lsm_weight: float = 0.0,
|
|
length_normalized_loss: bool = False,
|
|
report_cer: bool = True,
|
|
report_wer: bool = True,
|
|
sym_space: str = "<space>",
|
|
sym_blank: str = "<blank>",
|
|
# extract_feats_in_collect_stats: bool = True,
|
|
share_embedding: bool = False,
|
|
# preencoder: Optional[AbsPreEncoder] = None,
|
|
# postencoder: Optional[AbsPostEncoder] = None,
|
|
**kwargs,
|
|
):
|
|
|
|
super().__init__()
|
|
|
|
# audio encoder
|
|
hub = audio_encoder_conf.get("hub", None)
|
|
if hub == "ms":
|
|
from funasr import AutoModel
|
|
|
|
model = AutoModel(model=audio_encoder, model_revision="master")
|
|
# frontend = model.kwargs.get("frontend")
|
|
audio_encoder_output_size = model.model.encoder_output_size
|
|
|
|
audio_encoder = model.model.model.encoder
|
|
|
|
# self.frontend = frontend
|
|
|
|
elif hub == "hf":
|
|
pass
|
|
else:
|
|
encoder_class = tables.encoder_classes.get(audio_encoder)
|
|
audio_encoder = encoder_class(input_size=input_size, **audio_encoder_conf)
|
|
audio_encoder_output_size = audio_encoder.output_size()
|
|
freeze = audio_encoder_conf.get("freeze", True)
|
|
if freeze:
|
|
for name, param in audio_encoder.named_parameters():
|
|
param.requires_grad = False
|
|
audio_encoder.eval()
|
|
|
|
self.audio_encoder = audio_encoder
|
|
|
|
# llm
|
|
hub = llm_conf.get("hub", "hf")
|
|
self.llm = None
|
|
if hub == "hf":
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
|
|
|
|
init_param_path = llm_conf.get("init_param_path", "vicuna-7b-v1.5")
|
|
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
init_param_path,
|
|
load_in_8bit=None,
|
|
device_map=None,
|
|
use_cache=None,
|
|
)
|
|
freeze = llm_conf.get("freeze", True)
|
|
if freeze:
|
|
for name, param in model.named_parameters():
|
|
param.requires_grad = False
|
|
model.eval()
|
|
self.llm = model
|
|
|
|
# adaptor
|
|
adaptor_class = tables.adaptor_classes.get(audio_adaptor)
|
|
audio_adaptor_conf["encoder_dim"] = audio_encoder_output_size
|
|
audio_adaptor = adaptor_class(**audio_adaptor_conf)
|
|
|
|
self.audio_adaptor = audio_adaptor
|
|
|
|
self.error_calculator = None
|
|
|
|
self.length_normalized_loss = length_normalized_loss
|
|
self.beam_search = None
|
|
|
|
def forward(
|
|
self,
|
|
speech: torch.Tensor,
|
|
speech_lengths: torch.Tensor,
|
|
input_ids: torch.Tensor,
|
|
attention_mask: torch.Tensor,
|
|
labels_ids: torch.Tensor,
|
|
fbank_beg: torch.Tensor,
|
|
fbank_mask: torch.Tensor,
|
|
**kwargs,
|
|
) -> Tuple[torch.Tensor, Dict[str, torch.Tensor], torch.Tensor]:
|
|
"""Encoder + Decoder + Calc loss
|
|
Args:
|
|
speech: (Batch, Length, ...)
|
|
speech_lengths: (Batch, )
|
|
text: (Batch, Length)
|
|
text_lengths: (Batch,)
|
|
"""
|
|
# import pdb;
|
|
# pdb.set_trace()
|
|
if len(speech_lengths.size()) > 1:
|
|
speech_lengths = speech_lengths[:, 0]
|
|
|
|
batch_size, frames, _ = speech.shape
|
|
|
|
# audio encoder
|
|
encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)
|
|
|
|
# audio_adaptor
|
|
encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)
|
|
|
|
input_ids[input_ids < 0] = 0
|
|
inputs_embeds = self.llm.model.get_input_embeddings()(input_ids)
|
|
|
|
batch_size, token_num, dims = inputs_embeds.shape
|
|
fbank_mask[fbank_mask < 0] = 0
|
|
fbank_fake_lens = fbank_mask.sum(-1).to(torch.int32)
|
|
# _, l, _ = encoder_out.shape
|
|
for batch_idx in range(batch_size):
|
|
|
|
fbank_fake_len = fbank_fake_lens[batch_idx].item()
|
|
fbank_beg_idx = fbank_beg[batch_idx, 0].item()
|
|
min_len = min(fbank_fake_len, inputs_embeds.shape[1] - fbank_beg_idx)
|
|
|
|
try:
|
|
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
|
|
batch_idx, :min_len, :
|
|
]
|
|
except Exception as e:
|
|
logging.error(f"{str(e)}, {traceback.format_exc()}")
|
|
logging.info(
|
|
f"batch_idx: {batch_idx}, inputs_embeds: {inputs_embeds.shape}, fbank_beg_idx: {fbank_beg_idx}, min_len: {min_len}, fbank_fake_len: {fbank_fake_len}"
|
|
)
|
|
fbank_fake_len = encoder_out_lens[batch_idx].item()
|
|
min_len = min(fbank_fake_len, min_len)
|
|
inputs_embeds[batch_idx, fbank_beg_idx : fbank_beg_idx + min_len, :] = encoder_out[
|
|
batch_idx, :min_len, :
|
|
]
|
|
|
|
labels_ids[labels_ids == -1] = -100
|
|
|
|
model_outputs = self.llm(
|
|
inputs_embeds=inputs_embeds, attention_mask=attention_mask, labels=labels_ids
|
|
)
|
|
loss = model_outputs.loss
|
|
|
|
stats = {}
|
|
with torch.no_grad():
|
|
preds = torch.argmax(model_outputs.logits, -1)
|
|
acc_att = compute_accuracy(preds[:, :-1], labels_ids[:, 1:], ignore_label=-100)
|
|
stats["acc"] = acc_att
|
|
|
|
stats["loss"] = torch.clone(loss.detach())
|
|
stats["batch_size"] = batch_size
|
|
stats["batch_size_x_frames"] = frames * batch_size
|
|
stats["batch_size_real_frames"] = speech_lengths.sum().item()
|
|
stats["padding_frames"] = stats["batch_size_x_frames"] - stats["batch_size_real_frames"]
|
|
stats["batch_size_x_tokens"] = token_num * batch_size
|
|
stats["batch_size_real_tokens"] = attention_mask.sum().item()
|
|
stats["padding_tokens"] = stats["batch_size_x_tokens"] - stats["batch_size_real_tokens"]
|
|
|
|
# force_gatherable: to-device and to-tensor if scalar for DataParallel
|
|
if self.length_normalized_loss:
|
|
batch_size = int((labels_ids > 0 + 1).sum())
|
|
loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device)
|
|
return loss, stats, weight
|
|
|
|
def 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, **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:
|
|
data_src = load_audio_text_image_video(sub_str[1:], fs=frontend.fs)
|
|
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]
|
|
|
|
if kwargs.get("permute", True):
|
|
speech = speech.permute(0, 2, 1)
|
|
|
|
olens = 1 + (speech_lengths[0].item() - 3 + 2 * 1) // 2
|
|
olens = 1 + (olens - 3 + 2 * 1) // 2
|
|
sub_token_len = (olens - 1) // 2 + 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, **kwargs)
|
|
batch = to_device(output, kwargs["device"])
|
|
|
|
# audio encoder
|
|
speech = batch["speech"]
|
|
speech_lengths = batch["speech_lengths"][:, 0]
|
|
encoder_out, encoder_out_lens = self.audio_encoder(speech.permute(0, 2, 1), speech_lengths)
|
|
|
|
# audio_adaptor
|
|
encoder_out, encoder_out_lens = self.audio_adaptor(encoder_out, encoder_out_lens)
|
|
|
|
input_ids = 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, :
|
|
]
|
|
|
|
label = contents["assistant"][0]
|
|
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)
|
|
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 = []
|
|
result_i = {"key": key[0], "text": response, "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
|
|
|
|
return results, meta_data
|