whisper_small_s5k_b64_nofreeze_mgb2cv11
/
.ipynb_checkpoints
/run_speech_recognition_seq2seq_mixed_mgb2-cv1-checkpoint.py
#!/usr/bin/env python | |
# coding=utf-8 | |
# Copyright 2022 The HuggingFace Team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" | |
Fine-tuning the library models for sequence to sequence speech recognition | |
with 🤗 Datasets' streaming mode. | |
""" | |
# You can also adapt this script for your own sequence to sequence speech | |
# recognition task. Pointers for this are left as comments. | |
import logging | |
import os | |
import sys | |
from dataclasses import dataclass, field | |
from typing import Any, Dict, List, Optional, Union | |
import datasets | |
import torch | |
from datasets import DatasetDict, IterableDatasetDict, interleave_datasets, load_dataset | |
from datasets import Audio, interleave_datasets, IterableDataset | |
from torch.utils.data import IterableDataset | |
import evaluate | |
import transformers | |
from transformers import ( | |
AutoConfig, | |
AutoFeatureExtractor, | |
AutoModelForSpeechSeq2Seq, | |
AutoProcessor, | |
AutoTokenizer, | |
HfArgumentParser, | |
Seq2SeqTrainer, | |
Seq2SeqTrainingArguments, | |
TrainerCallback, | |
set_seed, | |
) | |
from transformers.models.whisper.english_normalizer import BasicTextNormalizer | |
from transformers.trainer_pt_utils import IterableDatasetShard | |
from transformers.trainer_utils import get_last_checkpoint, is_main_process | |
from transformers.utils import check_min_version, send_example_telemetry | |
from transformers.utils.versions import require_version | |
# Will error if the minimal version of Transformers is not installed. Remove at your own risks. | |
check_min_version("4.25.0.dev0") | |
require_version( | |
"datasets>=1.18.2", | |
"To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt", | |
) | |
logger = logging.getLogger(__name__) | |
class ModelArguments: | |
""" | |
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. | |
""" | |
model_name_or_path: str = field( | |
metadata={ | |
"help": "Path to pretrained model or model identifier from huggingface.co/models" | |
} | |
) | |
config_name: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": "Pretrained config name or path if not the same as model_name" | |
}, | |
) | |
tokenizer_name: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": "Pretrained tokenizer name or path if not the same as model_name" | |
}, | |
) | |
feature_extractor_name: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": "feature extractor name or path if not the same as model_name" | |
}, | |
) | |
cache_dir: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": "Where to store the pretrained models downloaded from huggingface.co" | |
}, | |
) | |
use_fast_tokenizer: bool = field( | |
default=True, | |
metadata={ | |
"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not." | |
}, | |
) | |
model_revision: str = field( | |
default="main", | |
metadata={ | |
"help": "The specific model version to use (can be a branch name, tag name or commit id)." | |
}, | |
) | |
use_auth_token: bool = field( | |
default=False, | |
metadata={ | |
"help": ( | |
"Will use the token generated when running `huggingface-cli login` (necessary to use this script " | |
"with private models)." | |
) | |
}, | |
) | |
freeze_feature_encoder: bool = field( | |
default=True, | |
metadata={"help": "Whether to freeze the feature encoder layers of the model."}, | |
) | |
freeze_encoder: bool = field( | |
default=False, | |
metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}, | |
) | |
forced_decoder_ids: List[List[int]] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"A list of pairs of integers which indicates a mapping from generation indices to token indices " | |
"that will be forced before sampling. For example, [[0, 123]] means the first generated token " | |
"will always be a token of index 123." | |
) | |
}, | |
) | |
suppress_tokens: List[int] = field( | |
default=None, | |
metadata={"help": "A list of tokens that will be suppressed at generation."}, | |
) | |
model_index_name: str = field( | |
default=None, metadata={"help": "Pretty name for the model card."} | |
) | |
class DataTrainingArguments: | |
""" | |
Arguments pertaining to what data we are going to input our model for training and eval. | |
""" | |
dataset_name: str = field( | |
default=None, | |
metadata={"help": "The name of the dataset to use (via the datasets library)."}, | |
) | |
dataset_config_name: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": "The configuration name of the dataset to use (via the datasets library)." | |
}, | |
) | |
text_column: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": "The name of the column in the datasets containing the full texts (for summarization)." | |
}, | |
) | |
max_train_samples: Optional[int] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"For debugging purposes or quicker training, truncate the number of training examples to this " | |
"value if set." | |
) | |
}, | |
) | |
max_eval_samples: Optional[int] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"For debugging purposes or quicker training, truncate the number of evaluation examples to this " | |
"value if set." | |
) | |
}, | |
) | |
audio_column_name: str = field( | |
default="audio", | |
metadata={ | |
"help": "The name of the dataset column containing the audio data. Defaults to 'audio'" | |
}, | |
) | |
text_column_name: str = field( | |
default="text", | |
metadata={ | |
"help": "The name of the dataset column containing the text data. Defaults to 'text'" | |
}, | |
) | |
max_duration_in_seconds: float = field( | |
default=20.0, | |
metadata={ | |
"help": ( | |
"Truncate audio files that are longer than `max_duration_in_seconds` seconds to" | |
" 'max_duration_in_seconds`" | |
) | |
}, | |
) | |
min_duration_in_seconds: float = field( | |
default=0.0, | |
metadata={ | |
"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds" | |
}, | |
) | |
train_split_name: str = field( | |
default="train", | |
metadata={ | |
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" | |
}, | |
) | |
eval_split_name: str = field( | |
default="test", | |
metadata={ | |
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'" | |
}, | |
) | |
do_lower_case: bool = field( | |
default=False, | |
metadata={"help": "Whether the target text should be lower cased."}, | |
) | |
do_remove_punctuation: bool = field( | |
default=False, | |
metadata={"help": "Whether the target text should be striped of punctuation."}, | |
) | |
do_normalize_eval: bool = field( | |
default=True, | |
metadata={ | |
"help": "Whether to normalise the references and predictions in the eval WER calculation." | |
}, | |
) | |
language: str = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning " | |
"only. For English speech recognition, it should be set to `None`." | |
) | |
}, | |
) | |
task: str = field( | |
default="transcribe", | |
metadata={ | |
"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation." | |
}, | |
) | |
shuffle_buffer_size: Optional[int] = field( | |
default=500, | |
metadata={ | |
"help": ( | |
"The number of streamed examples to download before shuffling them. The large the buffer, " | |
"the closer it is to real offline shuffling." | |
) | |
}, | |
) | |
streaming: bool = field( | |
default=True, | |
metadata={ | |
"help": "Whether to use streaming mode to load and pre-process the data." | |
}, | |
) | |
class DataCollatorSpeechSeq2SeqWithPadding: | |
""" | |
Data collator that will dynamically pad the inputs received. | |
Args: | |
processor ([`WhisperProcessor`]) | |
The processor used for processing the data. | |
decoder_start_token_id (`int`) | |
The begin-of-sentence of the decoder. | |
""" | |
processor: Any | |
decoder_start_token_id: int | |
def __call__( | |
self, features: List[Dict[str, Union[List[int], torch.Tensor]]] | |
) -> Dict[str, torch.Tensor]: | |
# split inputs and labels since they have to be of different lengths and need | |
# different padding methods | |
model_input_name = self.processor.model_input_names[0] | |
input_features = [ | |
{model_input_name: feature[model_input_name]} for feature in features | |
] | |
label_features = [{"input_ids": feature["labels"]} for feature in features] | |
batch = self.processor.feature_extractor.pad( | |
input_features, return_tensors="pt" | |
) | |
labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt") | |
# replace padding with -100 to ignore loss correctly | |
labels = labels_batch["input_ids"].masked_fill( | |
labels_batch.attention_mask.ne(1), -100 | |
) | |
# if bos token is appended in previous tokenization step, | |
# cut bos token here as it's append later anyways | |
if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item(): | |
labels = labels[:, 1:] | |
batch["labels"] = labels | |
return batch | |
def load_maybe_streaming_dataset( | |
dataset_name, dataset_config_name, split="train", streaming=True, **kwargs | |
): | |
""" | |
Utility function to load a dataset in streaming mode. For datasets with multiple splits, | |
each split is loaded individually and then splits combined by taking alternating examples from | |
each (interleaving). | |
""" | |
if "+" in split: | |
# load multiple splits separated by the `+` symbol with streaming mode | |
dataset_splits = [ | |
load_dataset( | |
dataset_name, | |
dataset_config_name, | |
split=split_name, | |
streaming=streaming, | |
**kwargs, | |
) | |
for split_name in split.split("+") | |
] | |
# interleave multiple splits to form one dataset | |
interleaved_dataset = interleave_datasets(dataset_splits) | |
return interleaved_dataset | |
else: | |
# load a single split *with* streaming mode | |
dataset = load_dataset( | |
dataset_name, | |
dataset_config_name, | |
split=split, | |
streaming=streaming, | |
**kwargs, | |
) | |
return dataset | |
def load_multiple_streaming_datasets( | |
dataset_names: List, | |
dataset_config_names: List, | |
splits: Optional[List] = None, | |
text_column_names: Optional[List] = None, | |
sampling_rate: Optional[int] = 16000, | |
stopping_strategy: Optional[str] = "first_exhausted", | |
**kwargs | |
) -> IterableDataset: | |
if len(dataset_names) != len(dataset_config_names): | |
raise ValueError( | |
f"Ensure one config is passed for each dataset, got {len(dataset_names)} datasets and" | |
f" {len(dataset_config_names)} configs." | |
) | |
if splits is not None and len(splits) != len(dataset_names): | |
raise ValueError( | |
f"Ensure one split is passed for each dataset, got {len(dataset_names)} datasets and {len(splits)} splits." | |
) | |
if text_column_names is not None and len(text_column_names) != len(dataset_names): | |
raise ValueError( | |
f"Ensure one text column name is passed for each dataset, got {len(dataset_names)} datasets and" | |
f" {len(text_column_names)} text column names." | |
) | |
splits = splits if splits is not None else ["train" for i in range(len(dataset_names))] | |
text_column_names = ( | |
text_column_names if text_column_names is not None else ["text" for i in range(len(dataset_names))] | |
) | |
all_datasets = [] | |
# iterate over the datasets we want to interleave | |
for i, dataset_name in enumerate(dataset_names): | |
dataset = load_dataset(dataset_name, dataset_config_names[i], split=splits[i], streaming=True, **kwargs) | |
print(dataset) | |
# resample to specified sampling rate | |
dataset = dataset.cast_column("audio", Audio(sampling_rate)) | |
# normalise columns to ["audio", "sentence"] | |
if text_column_names[i] != "sentence": | |
dataset = dataset.rename_column(text_column_names[i], "sentence") | |
dataset = dataset.remove_columns(set(dataset.features.keys()) - set(["audio", "sentence"])) | |
all_datasets.append(dataset) | |
print("DATASET:",dataset.features.keys()) | |
interleaved_dataset = interleave_datasets(all_datasets, stopping_strategy=stopping_strategy) | |
return interleaved_dataset | |
def main(): | |
# 1. Parse input arguments | |
# See all possible arguments in src/transformers/training_args.py | |
# or by passing the --help flag to this script. | |
# We now keep distinct sets of args, for a cleaner separation of concerns. | |
parser = HfArgumentParser( | |
(ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments) | |
) | |
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): | |
# If we pass only one argument to the script and it's the path to a json file, | |
# let's parse it to get our arguments. | |
model_args, data_args, training_args = parser.parse_json_file( | |
json_file=os.path.abspath(sys.argv[1]) | |
) | |
else: | |
model_args, data_args, training_args = parser.parse_args_into_dataclasses() | |
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The | |
# information sent is the one passed as arguments along with your Python/PyTorch versions. | |
send_example_telemetry( | |
"run_speech_recognition_seq2seq_streaming", model_args, data_args | |
) | |
# 2. Setup logging | |
logging.basicConfig( | |
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", | |
datefmt="%m/%d/%Y %H:%M:%S", | |
handlers=[logging.StreamHandler(sys.stdout)], | |
) | |
log_level = training_args.get_process_log_level() | |
logger.setLevel(log_level) | |
datasets.utils.logging.set_verbosity(log_level) | |
transformers.utils.logging.set_verbosity(log_level) | |
transformers.utils.logging.enable_default_handler() | |
transformers.utils.logging.enable_explicit_format() | |
logger.setLevel( | |
logging.INFO if is_main_process(training_args.local_rank) else logging.WARN | |
) | |
# Log on each process the small summary: | |
logger.warning( | |
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}" | |
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" | |
) | |
logger.info(f"Training/evaluation parameters {training_args}") | |
# Set the verbosity to info of the Transformers logger (on main process only): | |
if is_main_process(training_args.local_rank): | |
transformers.utils.logging.set_verbosity_info() | |
logger.info("Training/evaluation parameters %s", training_args) | |
# 3. Detecting last checkpoint and eventually continue from last checkpoint | |
last_checkpoint = None | |
if ( | |
os.path.isdir(training_args.output_dir) | |
and training_args.do_train | |
and not training_args.overwrite_output_dir | |
): | |
last_checkpoint = get_last_checkpoint(training_args.output_dir) | |
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0: | |
raise ValueError( | |
f"Output directory ({training_args.output_dir}) already exists and is not empty. " | |
"Use --overwrite_output_dir to overcome." | |
) | |
elif ( | |
last_checkpoint is not None and training_args.resume_from_checkpoint is None | |
): | |
logger.info( | |
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change " | |
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." | |
) | |
# Set seed before initializing model. | |
set_seed(training_args.seed) | |
# 4. Load dataset | |
raw_datasets = IterableDatasetDict() | |
dataset_names = ["mozilla-foundation/common_voice_11_0", "arbml/mgb2_speech", ] | |
dataset_config_names = ["ar", "ar", ] | |
text_column_names = ["sentence", "text",] | |
if training_args.do_train: | |
raw_datasets["train"] = load_multiple_streaming_datasets( | |
dataset_names, | |
dataset_config_names=dataset_config_names, | |
text_column_names=text_column_names, | |
splits=["train","train"], | |
use_auth_token=True | |
) | |
if training_args.do_eval: | |
raw_datasets["eval"] = load_multiple_streaming_datasets( | |
dataset_names, | |
dataset_config_names=dataset_config_names, | |
text_column_names=text_column_names, | |
splits=["validation","validation"], | |
use_auth_token=True | |
) | |
raw_datasets_features = list(next(iter(raw_datasets.values())).features.keys()) | |
if data_args.audio_column_name not in raw_datasets_features: | |
raise ValueError( | |
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. " | |
"Make sure to set `--audio_column_name` to the correct audio column - one of " | |
f"{', '.join(raw_datasets_features)}." | |
) | |
# if data_args.text_column_name not in raw_datasets_features: | |
# print("raw_datasets_features:",raw_datasets_features) | |
# raise ValueError( | |
# f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. " | |
# "Make sure to set `--text_column_name` to the correct text column - one of " | |
# f"{', '.join(raw_datasets_features)}." | |
# ) | |
# 5. Load pretrained model, tokenizer, and feature extractor | |
# | |
# Distributed training: | |
# The .from_pretrained methods guarantee that only one local process can concurrently | |
config = AutoConfig.from_pretrained( | |
model_args.config_name | |
if model_args.config_name | |
else model_args.model_name_or_path, | |
cache_dir=model_args.cache_dir, | |
revision=model_args.model_revision, | |
use_auth_token=True if model_args.use_auth_token else None, | |
) | |
config.update( | |
{ | |
"forced_decoder_ids": model_args.forced_decoder_ids, | |
"suppress_tokens": model_args.suppress_tokens, | |
} | |
) | |
if training_args.gradient_checkpointing: | |
config.update({"use_cache": False}) | |
feature_extractor = AutoFeatureExtractor.from_pretrained( | |
model_args.feature_extractor_name | |
if model_args.feature_extractor_name | |
else model_args.model_name_or_path, | |
cache_dir=model_args.cache_dir, | |
revision=model_args.model_revision, | |
use_auth_token=True if model_args.use_auth_token else None, | |
) | |
tokenizer = AutoTokenizer.from_pretrained( | |
model_args.tokenizer_name | |
if model_args.tokenizer_name | |
else model_args.model_name_or_path, | |
cache_dir=model_args.cache_dir, | |
use_fast=model_args.use_fast_tokenizer, | |
revision=model_args.model_revision, | |
use_auth_token=True if model_args.use_auth_token else None, | |
) | |
model = AutoModelForSpeechSeq2Seq.from_pretrained( | |
model_args.model_name_or_path, | |
config=config, | |
cache_dir=model_args.cache_dir, | |
revision=model_args.model_revision, | |
use_auth_token=True if model_args.use_auth_token else None, | |
) | |
if model.config.decoder_start_token_id is None: | |
raise ValueError( | |
"Make sure that `config.decoder_start_token_id` is correctly defined" | |
) | |
max_label_length = model.config.max_length | |
if model_args.freeze_feature_encoder: | |
model.freeze_feature_encoder() | |
if model_args.freeze_encoder: | |
model.freeze_encoder() | |
model.model.encoder.gradient_checkpointing = False | |
if data_args.language is not None: | |
# We only need to set the task id when the language is specified (i.e. in a multilingual setting) | |
tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task) | |
# 6. Resample speech dataset if necessary | |
dataset_sampling_rate = ( | |
next(iter(raw_datasets.values())) | |
.features[data_args.audio_column_name] | |
.sampling_rate | |
) | |
if dataset_sampling_rate != feature_extractor.sampling_rate: | |
raw_datasets = raw_datasets.cast_column( | |
data_args.audio_column_name, | |
datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate), | |
) | |
# 7. Preprocessing the datasets. | |
# We need to read the audio files as arrays and tokenize the targets. | |
max_input_length = ( | |
data_args.max_duration_in_seconds * feature_extractor.sampling_rate | |
) | |
min_input_length = ( | |
data_args.min_duration_in_seconds * feature_extractor.sampling_rate | |
) | |
audio_column_name = data_args.audio_column_name | |
text_column_name = data_args.text_column_name | |
model_input_name = feature_extractor.model_input_names[0] | |
do_lower_case = data_args.do_lower_case | |
do_remove_punctuation = data_args.do_remove_punctuation | |
normalizer = BasicTextNormalizer() # 'official' text normalizer from OpenAI | |
if data_args.max_train_samples is not None: | |
raw_datasets["train"] = raw_datasets["train"].take(data_args.max_train_samples) | |
if data_args.max_eval_samples is not None: | |
raw_datasets["eval"] = raw_datasets["eval"].select( | |
range(data_args.max_eval_samples) | |
) | |
def prepare_dataset(batch): | |
# process audio | |
sample = batch[audio_column_name] | |
inputs = feature_extractor( | |
sample["array"], sampling_rate=sample["sampling_rate"] | |
) | |
# process audio length | |
batch[model_input_name] = inputs.get(model_input_name)[0] | |
batch["input_length"] = len(sample["array"]) | |
# process targets | |
input_str = ( | |
batch[text_column_name].lower() | |
if do_lower_case | |
else batch[text_column_name] | |
) | |
if do_remove_punctuation: | |
input_str = normalizer(input_str).strip() | |
batch["labels"] = tokenizer(input_str).input_ids | |
return batch | |
with training_args.main_process_first(desc="dataset map pre-processing"): | |
vectorized_datasets = raw_datasets.map( | |
prepare_dataset, | |
remove_columns=raw_datasets_features, | |
).with_format("torch") | |
if training_args.do_train: | |
vectorized_datasets["train"] = vectorized_datasets["train"].shuffle( | |
buffer_size=data_args.shuffle_buffer_size, | |
seed=training_args.seed, | |
) | |
# filter training data that is shorter than min_input_length or longer than | |
# max_input_length | |
def is_audio_in_length_range(length): | |
return min_input_length < length < max_input_length | |
vectorized_datasets["train"] = vectorized_datasets["train"].filter( | |
is_audio_in_length_range, | |
input_columns=["input_length"], | |
) | |
def filter_labels(labels): | |
"""Filter label sequences longer than max length""" | |
return len(labels) < max_label_length | |
vectorized_datasets = vectorized_datasets.filter( | |
filter_labels, input_columns=["labels"] | |
) | |
# 8. Load Metric | |
metric = evaluate.load("wer") | |
do_normalize_eval = data_args.do_normalize_eval | |
def compute_metrics(pred): | |
pred_ids = pred.predictions | |
pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id | |
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True) | |
# we do not want to group tokens when computing the metrics | |
label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True) | |
if do_normalize_eval: | |
pred_str = [normalizer(pred) for pred in pred_str] | |
label_str = [normalizer(label) for label in label_str] | |
# filtering step to only evaluate the samples that correspond to non-zero references: | |
pred_str = [ | |
pred_str[i] for i in range(len(pred_str)) if len(label_str[i]) > 0 | |
] | |
label_str = [ | |
label_str[i] for i in range(len(label_str)) if len(label_str[i]) > 0 | |
] | |
wer = 100 * metric.compute(predictions=pred_str, references=label_str) | |
return {"wer": wer} | |
# 9. Create a single speech processor | |
if is_main_process(training_args.local_rank): | |
# save feature extractor, tokenizer and config | |
feature_extractor.save_pretrained(training_args.output_dir) | |
tokenizer.save_pretrained(training_args.output_dir) | |
config.save_pretrained(training_args.output_dir) | |
processor = AutoProcessor.from_pretrained(training_args.output_dir) | |
# 10. Define data collator | |
data_collator = DataCollatorSpeechSeq2SeqWithPadding( | |
processor=processor, | |
decoder_start_token_id=model.config.decoder_start_token_id, | |
) | |
# 11. Configure Trainer | |
# Trainer callback to reinitialise and reshuffle the streamable datasets at the beginning of each epoch | |
# Only required for streaming: Trainer automatically shuffles non-streaming datasets | |
class ShuffleCallback(TrainerCallback): | |
def on_epoch_begin(self, args, state, control, train_dataloader, **kwargs): | |
if isinstance(train_dataloader.dataset, IterableDatasetShard): | |
pass # set_epoch() is handled by the Trainer | |
elif isinstance(train_dataloader.dataset, IterableDataset): | |
train_dataloader.dataset.set_epoch(train_dataloader.dataset._epoch + 1) | |
# Initialize Trainer | |
trainer = Seq2SeqTrainer( | |
model=model, | |
args=training_args, | |
train_dataset=vectorized_datasets["train"] if training_args.do_train else None, | |
eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None, | |
tokenizer=feature_extractor, | |
data_collator=data_collator, | |
compute_metrics=compute_metrics | |
if training_args.predict_with_generate | |
else None, | |
callbacks=[ShuffleCallback()], | |
) | |
# 12. Training | |
if training_args.do_train: | |
checkpoint = None | |
if training_args.resume_from_checkpoint is not None: | |
checkpoint = training_args.resume_from_checkpoint | |
elif last_checkpoint is not None: | |
checkpoint = last_checkpoint | |
train_result = trainer.train(resume_from_checkpoint=checkpoint) | |
trainer.save_model() # Saves the feature extractor too for easy upload | |
metrics = train_result.metrics | |
if data_args.max_train_samples: | |
metrics["train_samples"] = data_args.max_train_samples | |
trainer.log_metrics("train", metrics) | |
trainer.save_metrics("train", metrics) | |
trainer.save_state() | |
# 13. Evaluation | |
results = {} | |
if training_args.do_eval: | |
logger.info("*** Evaluate ***") | |
metrics = trainer.evaluate( | |
metric_key_prefix="eval", | |
max_length=training_args.generation_max_length, | |
num_beams=training_args.generation_num_beams, | |
) | |
if data_args.max_eval_samples: | |
metrics["eval_samples"] = data_args.max_eval_samples | |
trainer.log_metrics("eval", metrics) | |
trainer.save_metrics("eval", metrics) | |
# 14. Write Training Stats | |
kwargs = { | |
"finetuned_from": model_args.model_name_or_path, | |
"tasks": "automatic-speech-recognition", | |
"tags": "whisper-event", | |
} | |
if data_args.dataset_name is not None: | |
kwargs["dataset_tags"] = data_args.dataset_name | |
if data_args.dataset_config_name is not None: | |
kwargs[ | |
"dataset" | |
] = f"{data_args.dataset_name} {data_args.dataset_config_name}" | |
else: | |
kwargs["dataset"] = data_args.dataset_name | |
if "common_voice" in data_args.dataset_name: | |
kwargs["language"] = data_args.dataset_config_name[:2] | |
if model_args.model_index_name is not None: | |
kwargs["model_name"] = model_args.model_index_name | |
if training_args.push_to_hub: | |
trainer.push_to_hub(**kwargs) | |
else: | |
trainer.create_model_card(**kwargs) | |
return results | |
if __name__ == "__main__": | |
main() | |