mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
685 lines
25 KiB
Python
685 lines
25 KiB
Python
import torch
|
|
import logging
|
|
import traceback
|
|
import numpy as np
|
|
from funasr.register import tables
|
|
from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
|
|
|
|
|
|
@tables.register("dataset_classes", "SenseVoiceDataset")
|
|
class SenseVoiceDataset(torch.utils.data.Dataset):
|
|
"""
|
|
SenseVoiceDataset
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
path,
|
|
index_ds: str = None,
|
|
frontend=None,
|
|
tokenizer=None,
|
|
int_pad_value: int = -1,
|
|
float_pad_value: float = 0.0,
|
|
**kwargs,
|
|
):
|
|
super().__init__()
|
|
index_ds_class = tables.index_ds_classes.get(index_ds)
|
|
self.index_ds = index_ds_class(path, **kwargs)
|
|
preprocessor_speech = kwargs.get("preprocessor_speech", None)
|
|
if preprocessor_speech:
|
|
preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
|
|
preprocessor_speech = preprocessor_speech_class(
|
|
**kwargs.get("preprocessor_speech_conf")
|
|
)
|
|
self.preprocessor_speech = preprocessor_speech
|
|
preprocessor_text = kwargs.get("preprocessor_text", None)
|
|
if preprocessor_text:
|
|
preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
|
|
preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
|
|
self.preprocessor_text = preprocessor_text
|
|
|
|
self.frontend = frontend
|
|
self.fs = 16000 if frontend is None else frontend.fs
|
|
self.data_type = "sound"
|
|
self.tokenizer = tokenizer
|
|
|
|
self.int_pad_value = int_pad_value
|
|
self.float_pad_value = float_pad_value
|
|
self.sos = kwargs.get("sos", "<|startoftranscript|>")
|
|
self.eos = kwargs.get("eos", "<|endoftext|>")
|
|
self.batch_size = kwargs.get("batch_size")
|
|
self.batch_type = kwargs.get("batch_type")
|
|
self.prompt_ids_len = 0
|
|
self.retry = kwargs.get("retry", 5)
|
|
|
|
self.permute = False
|
|
from funasr.frontends.whisper_frontend import WhisperFrontend
|
|
|
|
if isinstance(self.frontend, WhisperFrontend):
|
|
self.permute = True
|
|
self.max_token_length = kwargs.get("max_token_length", 1500)
|
|
self.batch_size_scale_ratio_max = kwargs.get("batch_size_scale_ratio_max", 1.5)
|
|
self.batch_size_token_max = kwargs.get("batch_size_token_max", 2500)
|
|
self.multiturn_num_max = kwargs.get("multiturn_num_max", 5)
|
|
self.max_source_length = kwargs.get("max_source_length", 3000)
|
|
|
|
def get_source_len(self, index):
|
|
item = self.index_ds[index]
|
|
return self.index_ds.get_source_len(item)
|
|
|
|
def get_target_len(self, index):
|
|
item = self.index_ds[index]
|
|
return self.index_ds.get_target_len(item)
|
|
|
|
def __len__(self):
|
|
return len(self.index_ds)
|
|
|
|
def __getitem__(self, index):
|
|
|
|
output = None
|
|
for idx in range(self.retry):
|
|
if idx == 0:
|
|
index_cur = index
|
|
else:
|
|
index_cur = torch.randint(0, len(self.index_ds), ()).item()
|
|
|
|
item = self.index_ds[index_cur]
|
|
|
|
source = item["source"]
|
|
try:
|
|
data_src = load_audio_text_image_video(source, fs=self.fs)
|
|
except Exception as e:
|
|
logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")
|
|
continue
|
|
|
|
if self.preprocessor_speech:
|
|
data_src = self.preprocessor_speech(data_src, fs=self.fs)
|
|
speech, speech_lengths = extract_fbank(
|
|
data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
|
|
) # speech: [b, T, d]
|
|
if speech_lengths > self.max_source_length:
|
|
logging.info(
|
|
f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}"
|
|
)
|
|
continue
|
|
|
|
if self.permute:
|
|
speech = speech.permute(0, 2, 1)
|
|
target = item["target"]
|
|
if self.preprocessor_text:
|
|
target = self.preprocessor_text(target)
|
|
|
|
task = item.get("prompt", "<|ASR|>")
|
|
text_language = item.get("text_language", "<|zh|>")
|
|
|
|
if isinstance(self.sos, str):
|
|
prompt = f"{self.sos}{task}{text_language}"
|
|
prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
|
|
else:
|
|
prompt = f"{task}{text_language}"
|
|
prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
|
|
prompt_ids = [self.sos] + prompt_ids
|
|
|
|
prompt_ids_len = len(prompt_ids) - 1 # [sos, task]
|
|
self.prompt_ids_len = prompt_ids_len
|
|
|
|
target_ids = self.tokenizer.encode(target, allowed_special="all")
|
|
target_ids_len = len(target_ids) + 1 # [lid, text]
|
|
if target_ids_len > 200:
|
|
continue
|
|
|
|
if isinstance(self.eos, str):
|
|
eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
|
|
else:
|
|
eos = [self.eos]
|
|
|
|
ids = prompt_ids + target_ids + eos # [sos, task, lid, text, eos]
|
|
ids_lengths = len(ids)
|
|
|
|
text = torch.tensor(ids, dtype=torch.int64)
|
|
text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
|
|
|
|
target_mask = (
|
|
[0] * (prompt_ids_len) + [1] * (target_ids_len) + [1]
|
|
) # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1]
|
|
target_mask_lengths = len(target_mask)
|
|
target_mask = torch.tensor(target_mask, dtype=torch.float32)
|
|
target_mask_lengths = torch.tensor([target_mask_lengths], dtype=torch.int32)
|
|
|
|
output = {
|
|
"speech": speech[0, :, :],
|
|
"speech_lengths": speech_lengths,
|
|
"text": text,
|
|
"text_lengths": text_lengths,
|
|
"target_mask": target_mask,
|
|
"target_mask_lengths": target_mask_lengths,
|
|
}
|
|
break
|
|
|
|
return output
|
|
|
|
def collator(self, samples: list = None):
|
|
outputs = {}
|
|
for sample in samples:
|
|
if sample is None:
|
|
continue
|
|
for key in sample.keys():
|
|
if key not in outputs:
|
|
outputs[key] = []
|
|
outputs[key].append(sample[key])
|
|
|
|
if len(outputs) < 1:
|
|
logging.error(f"ERROR: data is empty!")
|
|
outputs = {
|
|
"speech": torch.rand((10, 128), dtype=torch.float32)[None, :, :],
|
|
"speech_lengths": torch.tensor(
|
|
[
|
|
10,
|
|
],
|
|
dtype=torch.int32,
|
|
)[:, None],
|
|
"text": torch.tensor(
|
|
[
|
|
58836,
|
|
],
|
|
dtype=torch.int32,
|
|
)[None, :],
|
|
"text_lengths": torch.tensor(
|
|
[
|
|
1,
|
|
],
|
|
dtype=torch.int32,
|
|
)[:, None],
|
|
"target_mask": torch.tensor([[0] * (self.prompt_ids_len) + [1] * (1) + [1]])[
|
|
None, :
|
|
],
|
|
}
|
|
return outputs
|
|
|
|
for key, data_list in outputs.items():
|
|
if isinstance(data_list[0], torch.Tensor):
|
|
if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
|
|
|
|
pad_value = self.int_pad_value
|
|
else:
|
|
pad_value = self.float_pad_value
|
|
|
|
outputs[key] = torch.nn.utils.rnn.pad_sequence(
|
|
data_list, batch_first=True, padding_value=pad_value
|
|
)
|
|
|
|
if self.batch_type != "example":
|
|
for i in range(10):
|
|
outputs = self._filter_badcase(outputs, i=i)
|
|
|
|
return outputs
|
|
|
|
def _filter_badcase(self, outputs, i=0):
|
|
b, t, _ = outputs["speech"].shape
|
|
|
|
if b * t > self.batch_size * 1.25:
|
|
beg = torch.randint(0, 2, ()).item()
|
|
if b < 2:
|
|
beg = 0
|
|
logging.info(
|
|
f"Warning, b * t: {b * t} > {self.batch_size}, drop half data {i}th, beg:{beg}"
|
|
)
|
|
for key, data_list in outputs.items():
|
|
outputs[key] = outputs[key][beg : beg + b : 2]
|
|
|
|
speech_lengths_max = outputs["speech_lengths"].max().item()
|
|
outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
|
|
text_lengths_max = outputs["text_lengths"].max().item()
|
|
outputs["text"] = outputs["text"][:, :text_lengths_max]
|
|
target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
|
|
outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
|
|
|
|
return outputs
|
|
|
|
|
|
@tables.register("dataset_classes", "SenseVoiceCTCDataset")
|
|
class SenseVoiceCTCDataset(torch.utils.data.Dataset):
|
|
"""
|
|
SenseVoiceCTCDataset
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
path,
|
|
index_ds: str = None,
|
|
frontend=None,
|
|
tokenizer=None,
|
|
int_pad_value: int = -1,
|
|
float_pad_value: float = 0.0,
|
|
**kwargs,
|
|
):
|
|
super().__init__()
|
|
index_ds_class = tables.index_ds_classes.get(index_ds)
|
|
self.index_ds = index_ds_class(path, **kwargs)
|
|
preprocessor_speech = kwargs.get("preprocessor_speech", None)
|
|
if preprocessor_speech:
|
|
preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
|
|
preprocessor_speech = preprocessor_speech_class(
|
|
**kwargs.get("preprocessor_speech_conf")
|
|
)
|
|
self.preprocessor_speech = preprocessor_speech
|
|
preprocessor_text = kwargs.get("preprocessor_text", None)
|
|
if preprocessor_text:
|
|
preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
|
|
preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
|
|
self.preprocessor_text = preprocessor_text
|
|
|
|
self.frontend = frontend
|
|
self.fs = 16000 if frontend is None else frontend.fs
|
|
self.data_type = "sound"
|
|
self.tokenizer = tokenizer
|
|
|
|
self.int_pad_value = int_pad_value
|
|
self.float_pad_value = float_pad_value
|
|
self.sos = kwargs.get("sos", "<|startoftranscript|>")
|
|
self.eos = kwargs.get("eos", "<|endoftext|>")
|
|
self.batch_size = kwargs.get("batch_size")
|
|
self.batch_type = kwargs.get("batch_type")
|
|
self.prompt_ids_len = 0
|
|
self.retry = kwargs.get("retry", 5)
|
|
|
|
self.permute = False
|
|
from funasr.frontends.whisper_frontend import WhisperFrontend
|
|
|
|
if isinstance(self.frontend, WhisperFrontend):
|
|
self.permute = True
|
|
|
|
def get_source_len(self, index):
|
|
item = self.index_ds[index]
|
|
return self.index_ds.get_source_len(item)
|
|
|
|
def get_target_len(self, index):
|
|
item = self.index_ds[index]
|
|
return self.index_ds.get_target_len(item)
|
|
|
|
def __len__(self):
|
|
return len(self.index_ds)
|
|
|
|
def __getitem__(self, index):
|
|
|
|
output = None
|
|
for idx in range(self.retry):
|
|
if idx == 0:
|
|
index_cur = index
|
|
else:
|
|
index_cur = torch.randint(0, len(self.index_ds), ()).item()
|
|
|
|
item = self.index_ds[index_cur]
|
|
|
|
source = item["source"]
|
|
try:
|
|
data_src = load_audio_text_image_video(source, fs=self.fs)
|
|
except Exception as e:
|
|
logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")
|
|
continue
|
|
|
|
if self.preprocessor_speech:
|
|
data_src = self.preprocessor_speech(data_src, fs=self.fs)
|
|
speech, speech_lengths = extract_fbank(
|
|
data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
|
|
) # speech: [b, T, d]
|
|
|
|
if speech_lengths > self.batch_size:
|
|
continue
|
|
if self.permute:
|
|
speech = speech.permute(0, 2, 1)
|
|
asr_target = item["target"]
|
|
if self.preprocessor_text:
|
|
asr_target = self.preprocessor_text(asr_target)
|
|
emo_target = item["emo_target"]
|
|
event_target = item["event_target"]
|
|
text_language = item.get("text_language", "<|zh|>")
|
|
punc_itn_bottom = item.get("with_or_wo_itn", "<|SPECIAL_TOKEN_13|>")
|
|
|
|
target_ids = self.tokenizer.encode(asr_target, allowed_special="all")
|
|
target_ids_len = len(target_ids) # [text]
|
|
if target_ids_len > 200:
|
|
continue
|
|
|
|
lid_ids = self.tokenizer.encode(text_language, allowed_special="all")
|
|
emo_ids = self.tokenizer.encode(emo_target, allowed_special="all")
|
|
event_ids = self.tokenizer.encode(event_target, allowed_special="all")
|
|
punc_itn_bottom_ids = self.tokenizer.encode(punc_itn_bottom, allowed_special="all")
|
|
|
|
ids = (
|
|
lid_ids + emo_ids + event_ids + punc_itn_bottom_ids + target_ids
|
|
) # [lid, emo, lid, itn, text]
|
|
ids_lengths = len(ids)
|
|
|
|
text = torch.tensor(ids, dtype=torch.int64)
|
|
text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
|
|
|
|
output = {
|
|
"speech": speech[0, :, :],
|
|
"speech_lengths": speech_lengths,
|
|
"text": text,
|
|
"text_lengths": text_lengths,
|
|
}
|
|
break
|
|
|
|
return output
|
|
|
|
def collator(self, samples: list = None):
|
|
outputs = {}
|
|
for sample in samples:
|
|
if sample is None:
|
|
continue
|
|
for key in sample.keys():
|
|
if key not in outputs:
|
|
outputs[key] = []
|
|
outputs[key].append(sample[key])
|
|
|
|
if len(outputs) < 1:
|
|
logging.error(f"ERROR: data is empty!")
|
|
outputs = {
|
|
"speech": torch.rand((10, 128), dtype=torch.float32)[None, :, :],
|
|
"speech_lengths": torch.tensor(
|
|
[
|
|
10,
|
|
],
|
|
dtype=torch.int32,
|
|
)[:, None],
|
|
"text": torch.tensor(
|
|
[
|
|
58836,
|
|
],
|
|
dtype=torch.int32,
|
|
)[None, :],
|
|
"text_lengths": torch.tensor(
|
|
[
|
|
1,
|
|
],
|
|
dtype=torch.int32,
|
|
)[:, None],
|
|
}
|
|
return outputs
|
|
|
|
for key, data_list in outputs.items():
|
|
if isinstance(data_list[0], torch.Tensor):
|
|
if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
|
|
|
|
pad_value = self.int_pad_value
|
|
else:
|
|
pad_value = self.float_pad_value
|
|
|
|
outputs[key] = torch.nn.utils.rnn.pad_sequence(
|
|
data_list, batch_first=True, padding_value=pad_value
|
|
)
|
|
|
|
if self.batch_type != "example":
|
|
for i in range(10):
|
|
outputs = self._filter_badcase(outputs, i=i)
|
|
|
|
return outputs
|
|
|
|
def _filter_badcase(self, outputs, i=0):
|
|
b, t, _ = outputs["speech"].shape
|
|
|
|
if b * t > self.batch_size * 1.25:
|
|
beg = torch.randint(0, 2, ()).item()
|
|
if b < 2:
|
|
beg = 0
|
|
logging.info(
|
|
f"Warning, b * t: {b * t} > {self.batch_size}, drop half data {i}th, beg:{beg}"
|
|
)
|
|
for key, data_list in outputs.items():
|
|
outputs[key] = outputs[key][beg : beg + b : 2]
|
|
|
|
speech_lengths_max = outputs["speech_lengths"].max().item()
|
|
outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
|
|
text_lengths_max = outputs["text_lengths"].max().item()
|
|
outputs["text"] = outputs["text"][:, :text_lengths_max]
|
|
|
|
return outputs
|
|
|
|
|
|
@tables.register("dataset_classes", "SenseVoicePitchDataset")
|
|
class SenseVoicePitchDataset(torch.utils.data.Dataset):
|
|
"""
|
|
SenseVoiceDataset
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
path,
|
|
index_ds: str = None,
|
|
frontend=None,
|
|
tokenizer=None,
|
|
int_pad_value: int = -1,
|
|
float_pad_value: float = 0.0,
|
|
**kwargs,
|
|
):
|
|
super().__init__()
|
|
index_ds_class = tables.index_ds_classes.get(index_ds)
|
|
self.index_ds = index_ds_class(path, **kwargs)
|
|
preprocessor_speech = kwargs.get("preprocessor_speech", None)
|
|
if preprocessor_speech:
|
|
preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
|
|
preprocessor_speech = preprocessor_speech_class(
|
|
**kwargs.get("preprocessor_speech_conf")
|
|
)
|
|
self.preprocessor_speech = preprocessor_speech
|
|
preprocessor_text = kwargs.get("preprocessor_text", None)
|
|
if preprocessor_text:
|
|
preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
|
|
preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
|
|
self.preprocessor_text = preprocessor_text
|
|
|
|
self.frontend = frontend
|
|
self.fs = 16000 if frontend is None else frontend.fs
|
|
self.data_type = "sound"
|
|
self.tokenizer = tokenizer
|
|
|
|
self.int_pad_value = int_pad_value
|
|
self.float_pad_value = float_pad_value
|
|
self.sos = kwargs.get("sos", "<|startoftranscript|>")
|
|
self.eos = kwargs.get("eos", "<|endoftext|>")
|
|
self.batch_size = kwargs.get("batch_size")
|
|
self.batch_type = kwargs.get("batch_type")
|
|
self.prompt_ids_len = 0
|
|
self.retry = kwargs.get("retry", 5)
|
|
|
|
self.permute = False
|
|
from funasr.frontends.whisper_frontend import WhisperFrontend
|
|
|
|
if isinstance(self.frontend, WhisperFrontend):
|
|
self.permute = True
|
|
self.max_token_length = kwargs.get("max_token_length", 1500)
|
|
self.batch_size_scale_ratio_max = kwargs.get("batch_size_scale_ratio_max", 1.5)
|
|
self.batch_size_token_max = kwargs.get("batch_size_token_max", 2500)
|
|
self.multiturn_num_max = kwargs.get("multiturn_num_max", 5)
|
|
self.max_source_length = kwargs.get("max_source_length", 3000)
|
|
|
|
def get_source_len(self, index):
|
|
item = self.index_ds[index]
|
|
return self.index_ds.get_source_len(item)
|
|
|
|
def get_target_len(self, index):
|
|
item = self.index_ds[index]
|
|
return self.index_ds.get_target_len(item)
|
|
|
|
def __len__(self):
|
|
return len(self.index_ds)
|
|
|
|
def __getitem__(self, index):
|
|
|
|
output = None
|
|
for idx in range(self.retry):
|
|
if idx == 0:
|
|
index_cur = index
|
|
else:
|
|
index_cur = torch.randint(0, len(self.index_ds), ()).item()
|
|
|
|
item = self.index_ds[index_cur]
|
|
|
|
source = item["source"]
|
|
try:
|
|
data_src = load_audio_text_image_video(source, fs=self.fs)
|
|
except Exception as e:
|
|
logging.error(f"Loading wav failed! {str(e)}, {traceback.format_exc()}")
|
|
continue
|
|
|
|
if self.preprocessor_speech:
|
|
data_src = self.preprocessor_speech(data_src, fs=self.fs)
|
|
speech, speech_lengths = extract_fbank(
|
|
data_src, data_type=self.data_type, frontend=self.frontend, is_final=True
|
|
) # speech: [b, T, d]
|
|
if speech_lengths > self.max_source_length:
|
|
logging.info(
|
|
f"speech_lengths > max_source_length: {speech_lengths}>{self.max_source_length}, {item}"
|
|
)
|
|
continue
|
|
|
|
if self.permute:
|
|
speech = speech.permute(0, 2, 1)
|
|
target = item["target"]
|
|
if self.preprocessor_text:
|
|
target = self.preprocessor_text(target)
|
|
|
|
task = item.get("prompt", "<|ASR|>")
|
|
text_language = item.get("text_language", "<|zh|>")
|
|
|
|
if isinstance(self.sos, str):
|
|
prompt = f"{self.sos}{task}{text_language}"
|
|
prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
|
|
else:
|
|
prompt = f"{task}{text_language}"
|
|
prompt_ids = self.tokenizer.encode(prompt, allowed_special="all")
|
|
prompt_ids = [self.sos] + prompt_ids
|
|
|
|
prompt_ids_len = len(prompt_ids) - 1 # [sos, task]
|
|
self.prompt_ids_len = prompt_ids_len
|
|
|
|
target_ids = self.tokenizer.encode(target, allowed_special="all")
|
|
target_ids_len = len(target_ids) + 1 # [lid, text]
|
|
if target_ids_len > 200:
|
|
continue
|
|
|
|
if isinstance(self.eos, str):
|
|
eos = self.tokenizer.encode(self.eos, allowed_special="all") # [eos]
|
|
else:
|
|
eos = [self.eos]
|
|
|
|
ids = prompt_ids + target_ids + eos # [sos, task, lid, text, eos]
|
|
ids_lengths = len(ids)
|
|
|
|
text = torch.tensor(ids, dtype=torch.int64)
|
|
text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
|
|
|
|
target_mask = (
|
|
[0] * (prompt_ids_len) + [1] * (target_ids_len) + [1]
|
|
) # [sos, task, lid, text, eos]: [0, 0, 1, 1, 1]
|
|
target_mask_lengths = len(target_mask)
|
|
target_mask = torch.tensor(target_mask, dtype=torch.float32)
|
|
target_mask_lengths = torch.tensor([target_mask_lengths], dtype=torch.int32)
|
|
|
|
# pitch
|
|
if 'f0' in item:
|
|
f0_file = item['f0']
|
|
f0 = torch.tensor(np.load(f0_file), dtype=torch.float32)
|
|
if f0.shape > speech.shape[1]:
|
|
f0 = f0[:speech.shape[1]]
|
|
elif f0.shape < speech.shape[1]:
|
|
last_value = f0[-1]
|
|
f0 = torch.cat([f0, last_value.repeat(speech.shape[1] - f0.shape)])
|
|
f0_tag = torch.tensor([1], dtype=torch.int32)
|
|
else:
|
|
f0 = torch.tensor([0.0], dtype=torch.float32)
|
|
f0_tag = torch.tensor([0], dtype=torch.int32)
|
|
|
|
output = {
|
|
"speech": speech[0, :, :],
|
|
"speech_lengths": speech_lengths,
|
|
"text": text,
|
|
"text_lengths": text_lengths,
|
|
"target_mask": target_mask,
|
|
"target_mask_lengths": target_mask_lengths,
|
|
"f0": f0,
|
|
"f0_tag": f0_tag,
|
|
}
|
|
break
|
|
|
|
return output
|
|
|
|
def collator(self, samples: list = None):
|
|
outputs = {}
|
|
for sample in samples:
|
|
if sample is None:
|
|
continue
|
|
for key in sample.keys():
|
|
if key not in outputs:
|
|
outputs[key] = []
|
|
outputs[key].append(sample[key])
|
|
|
|
if len(outputs) < 1:
|
|
logging.error(f"ERROR: data is empty!")
|
|
outputs = {
|
|
"speech": torch.rand((10, 128), dtype=torch.float32)[None, :, :],
|
|
"speech_lengths": torch.tensor(
|
|
[
|
|
10,
|
|
],
|
|
dtype=torch.int32,
|
|
)[:, None],
|
|
"text": torch.tensor(
|
|
[
|
|
58836,
|
|
],
|
|
dtype=torch.int32,
|
|
)[None, :],
|
|
"text_lengths": torch.tensor(
|
|
[
|
|
1,
|
|
],
|
|
dtype=torch.int32,
|
|
)[:, None],
|
|
"target_mask": torch.tensor([[0] * (self.prompt_ids_len) + [1] * (1) + [1]])[
|
|
None, :
|
|
],
|
|
}
|
|
return outputs
|
|
|
|
for key, data_list in outputs.items():
|
|
if isinstance(data_list[0], torch.Tensor):
|
|
if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
|
|
|
|
pad_value = self.int_pad_value
|
|
else:
|
|
pad_value = self.float_pad_value
|
|
|
|
outputs[key] = torch.nn.utils.rnn.pad_sequence(
|
|
data_list, batch_first=True, padding_value=pad_value
|
|
)
|
|
|
|
if self.batch_type != "example":
|
|
for i in range(10):
|
|
outputs = self._filter_badcase(outputs, i=i)
|
|
|
|
return outputs
|
|
|
|
def _filter_badcase(self, outputs, i=0):
|
|
b, t, _ = outputs["speech"].shape
|
|
|
|
if b * t > self.batch_size * 1.25:
|
|
beg = torch.randint(0, 2, ()).item()
|
|
if b < 2:
|
|
beg = 0
|
|
logging.info(
|
|
f"Warning, b * t: {b * t} > {self.batch_size}, drop half data {i}th, beg:{beg}"
|
|
)
|
|
for key, data_list in outputs.items():
|
|
outputs[key] = outputs[key][beg : beg + b : 2]
|
|
|
|
speech_lengths_max = outputs["speech_lengths"].max().item()
|
|
outputs["speech"] = outputs["speech"][:, :speech_lengths_max, :]
|
|
text_lengths_max = outputs["text_lengths"].max().item()
|
|
outputs["text"] = outputs["text"][:, :text_lengths_max]
|
|
target_mask_lengths_max = outputs["target_mask_lengths"].max().item()
|
|
outputs["target_mask"] = outputs["target_mask"][:, :target_mask_lengths_max]
|
|
|
|
return outputs |