esc-bencher
commited on
Commit
•
0e8995d
1
Parent(s):
58673c4
Add training scripts and weights
Browse files- .gitattributes +1 -0
- README.md +47 -0
- medium.en.whisper +3 -0
- run_speech_recognition_whisper.py +747 -0
- run_switchboard.sh +35 -0
.gitattributes
CHANGED
@@ -30,3 +30,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.whisper filter=lfs diff=lfs merge=lfs -text
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README.md
ADDED
@@ -0,0 +1,47 @@
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---
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language:
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- en
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tags:
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- esc
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datasets:
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- switchboard
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---
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To reproduce this run, execute:
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```python
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#!/usr/bin/env bash
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CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
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--model_name_or_path="medium.en" \
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--dataset_name="esc-benchmark/esc-datasets" \
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--dataset_config_name="switchboard" \
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--max_steps="5000" \
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--output_dir="./" \
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--run_name="whisper-switchboard" \
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--max_steps="5000" \
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--output_dir="./" \
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--run_name="whisper-switchboard" \
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--wandb_project="whisper" \
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--per_device_train_batch_size="64" \
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--per_device_eval_batch_size="16" \
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--logging_steps="25" \
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--learning_rate="1e-4" \
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--warmup_steps="500" \
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--report_to="wandb" \
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--preprocessing_num_workers="16" \
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--evaluation_strategy="steps" \
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--eval_steps="1000" \
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--save_strategy="steps" \
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--save_steps="1000" \
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--generation_max_length="224" \
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--length_column_name="input_lengths" \
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--gradient_checkpointing \
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--group_by_length \
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--freeze_encoder \
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--fp16 \
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--overwrite_output_dir \
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--do_train \
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--do_eval \
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--do_predict \
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--predict_with_generate \
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--use_auth_token
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```
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medium.en.whisper
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:4a667fb55dfa9d15928c4169d324d45f59e371fafcd41661c8e54da370c8a415
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size 3055771163
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run_speech_recognition_whisper.py
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@@ -0,0 +1,747 @@
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#!/usr/bin/env python
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# coding=utf-8
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#
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4 |
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
|
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
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"""
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Fine-tuning OpenAI Whisper models for speech recognition.
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"""
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# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
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# flake8: noqa: E501
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import logging
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import os
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import whisper
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import sys
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from dataclasses import dataclass, field
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import tempfile
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+
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from typing import Optional, Dict, Union, List
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29 |
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import numpy as np
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import torch
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+
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import datasets
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34 |
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from datasets import DatasetDict, load_dataset
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35 |
+
import transformers
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36 |
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from torch import nn
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37 |
+
from transformers import (
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38 |
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HfArgumentParser,
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39 |
+
Seq2SeqTrainingArguments,
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40 |
+
set_seed,
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41 |
+
Seq2SeqTrainer,
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42 |
+
)
|
43 |
+
from transformers.trainer_utils import get_last_checkpoint, is_main_process
|
44 |
+
from transformers.utils import check_min_version
|
45 |
+
from transformers.utils.versions import require_version
|
46 |
+
|
47 |
+
import wandb
|
48 |
+
|
49 |
+
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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50 |
+
check_min_version("4.17.0.dev0")
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51 |
+
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52 |
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require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
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53 |
+
|
54 |
+
logger = logging.getLogger(__name__)
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55 |
+
|
56 |
+
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57 |
+
@dataclass
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58 |
+
class ModelArguments:
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59 |
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"""
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60 |
+
Arguments pertaining to which model/tokenizer we are going to fine-tune from.
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61 |
+
"""
|
62 |
+
model_name_or_path: Optional[str] = field(
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63 |
+
default=None,
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64 |
+
metadata={"help": "Path to pretrained model or model identifier from OpenAI Whisper NGC."}
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65 |
+
)
|
66 |
+
cache_dir: Optional[str] = field(
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67 |
+
default=None,
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68 |
+
metadata={"help": "Where to store the pretrained models downloaded from huggingface.co or OpenAI Whisper NGC."},
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69 |
+
)
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70 |
+
use_auth_token: bool = field(
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71 |
+
default=False,
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72 |
+
metadata={
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73 |
+
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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74 |
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"with private models)."
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75 |
+
},
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76 |
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)
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77 |
+
manifest_path: str = field(
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78 |
+
default="data",
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79 |
+
metadata={
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80 |
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"help": "Manifest path."
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81 |
+
},
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82 |
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)
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83 |
+
tokenizer_path: str = field(
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84 |
+
default="tokenizers",
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85 |
+
metadata={
|
86 |
+
"help": "Tokenizer path."
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87 |
+
},
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88 |
+
)
|
89 |
+
freeze_encoder: bool = field(
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90 |
+
default=False,
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91 |
+
metadata={"help": "Freeze the acoustic encoder of the model. Recommend when fine-tuning on small datasets."}
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92 |
+
)
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93 |
+
num_beams: int = field(
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94 |
+
default=1,
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95 |
+
metadata={"help": "Number of beams for evaluation."},
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96 |
+
)
|
97 |
+
length_penalty: float = field(
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98 |
+
default=1.0,
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99 |
+
metadata={"help": "Length penalty for evaluation."},
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100 |
+
)
|
101 |
+
use_adam8bit: bool = field(
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102 |
+
default=False,
|
103 |
+
metadata={"help": "Whether to use bitsandbytes 8bit AdamW optimiser."}
|
104 |
+
)
|
105 |
+
dropout_rate: float = field(
|
106 |
+
default=0.0,
|
107 |
+
metadata={"help": "The dropout ratio for all dropout layers (default=0)."}
|
108 |
+
)
|
109 |
+
|
110 |
+
|
111 |
+
@dataclass
|
112 |
+
class DataTrainingArguments:
|
113 |
+
"""
|
114 |
+
Arguments pertaining to what data we are going to input our model for training and eval.
|
115 |
+
"""
|
116 |
+
|
117 |
+
dataset_name: str = field(
|
118 |
+
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
|
119 |
+
)
|
120 |
+
dataset_config_name: Optional[str] = field(
|
121 |
+
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
|
122 |
+
)
|
123 |
+
text_column: Optional[str] = field(
|
124 |
+
default=None,
|
125 |
+
metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
|
126 |
+
)
|
127 |
+
dataset_cache_dir: Optional[str] = field(
|
128 |
+
default=None, metadata={"help": "Path to cache directory for saving and loading datasets"}
|
129 |
+
)
|
130 |
+
overwrite_cache: bool = field(
|
131 |
+
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
|
132 |
+
)
|
133 |
+
preprocessing_num_workers: Optional[int] = field(
|
134 |
+
default=None,
|
135 |
+
metadata={"help": "The number of processes to use for the preprocessing."},
|
136 |
+
)
|
137 |
+
max_train_samples: Optional[int] = field(
|
138 |
+
default=None,
|
139 |
+
metadata={
|
140 |
+
"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
|
141 |
+
"value if set."
|
142 |
+
},
|
143 |
+
)
|
144 |
+
max_eval_samples: Optional[int] = field(
|
145 |
+
default=None,
|
146 |
+
metadata={
|
147 |
+
"help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
|
148 |
+
"value if set."
|
149 |
+
},
|
150 |
+
)
|
151 |
+
max_predict_samples: Optional[int] = field(
|
152 |
+
default=None,
|
153 |
+
metadata={
|
154 |
+
"help": "For debugging purposes or quicker training, truncate the number of test examples to this "
|
155 |
+
"value if set."
|
156 |
+
},
|
157 |
+
)
|
158 |
+
audio_column_name: str = field(
|
159 |
+
default="audio",
|
160 |
+
metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
|
161 |
+
)
|
162 |
+
text_column_name: str = field(
|
163 |
+
default="text",
|
164 |
+
metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
|
165 |
+
)
|
166 |
+
max_duration_in_seconds: float = field(
|
167 |
+
default=20.0,
|
168 |
+
metadata={
|
169 |
+
"help": "Truncate training audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
|
170 |
+
},
|
171 |
+
)
|
172 |
+
min_duration_in_seconds: float = field(
|
173 |
+
default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
|
174 |
+
)
|
175 |
+
max_eval_duration_in_seconds: float = field(
|
176 |
+
default=None,
|
177 |
+
metadata={
|
178 |
+
"help": "Truncate eval/test audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
|
179 |
+
},
|
180 |
+
)
|
181 |
+
max_target_length: Optional[int] = field(
|
182 |
+
default=128,
|
183 |
+
metadata={
|
184 |
+
"help": "The maximum total sequence length for target text after tokenization. Sequences longer "
|
185 |
+
"than this will be truncated, sequences shorter will be padded."
|
186 |
+
},
|
187 |
+
)
|
188 |
+
min_target_length: Optional[int] = field(
|
189 |
+
default=0,
|
190 |
+
metadata={
|
191 |
+
"help": "The minimum total sequence length for target text after tokenization. Sequences shorter "
|
192 |
+
"than this will be filtered."
|
193 |
+
},
|
194 |
+
)
|
195 |
+
preprocessing_only: bool = field(
|
196 |
+
default=False,
|
197 |
+
metadata={
|
198 |
+
"help": "Whether to only do data preprocessing and skip training. "
|
199 |
+
"This is especially useful when data preprocessing errors out in distributed training due to timeout. "
|
200 |
+
"In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
|
201 |
+
"so that the cached datasets can consequently be loaded in distributed training"
|
202 |
+
},
|
203 |
+
)
|
204 |
+
train_split_name: str = field(
|
205 |
+
default="train",
|
206 |
+
metadata={
|
207 |
+
"help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
|
208 |
+
},
|
209 |
+
)
|
210 |
+
eval_split_name: str = field(
|
211 |
+
default="validation",
|
212 |
+
metadata={
|
213 |
+
"help": "The name of the evaluation data set split to use (via the datasets library). Defaults to 'validation'"
|
214 |
+
},
|
215 |
+
)
|
216 |
+
test_split_name: str = field(
|
217 |
+
default="test",
|
218 |
+
metadata={"help": "The name of the test data set split to use (via the datasets library). Defaults to 'test'"},
|
219 |
+
)
|
220 |
+
wandb_project: str = field(
|
221 |
+
default="speech-recognition-whisper",
|
222 |
+
metadata={"help": "The name of the wandb project."},
|
223 |
+
)
|
224 |
+
|
225 |
+
|
226 |
+
def write_wandb_pred(pred_str, label_str, prefix="eval"):
|
227 |
+
# convert str data to a wandb compatible format
|
228 |
+
str_data = [[label_str[i], pred_str[i]] for i in range(len(pred_str))]
|
229 |
+
# we'll log all predictions for the last epoch
|
230 |
+
wandb.log(
|
231 |
+
{
|
232 |
+
f"{prefix}/predictions": wandb.Table(
|
233 |
+
columns=["label_str", "pred_str"], data=str_data
|
234 |
+
)
|
235 |
+
},
|
236 |
+
)
|
237 |
+
|
238 |
+
|
239 |
+
def to_pad_to_mel(array):
|
240 |
+
"""Static function which:
|
241 |
+
1. Pads/trims a list of audio arrays to a max length of 30s
|
242 |
+
2. Computes log-mel filter coefficients from padded/trimmed audio sequences
|
243 |
+
Inputs:
|
244 |
+
array: list of audio arrays
|
245 |
+
Returns:
|
246 |
+
input_ids: torch.tensor of log-mel filter bank coefficients
|
247 |
+
"""
|
248 |
+
padded_input = whisper.pad_or_trim(np.asarray(array, dtype=np.float32))
|
249 |
+
input_ids = whisper.log_mel_spectrogram(padded_input)
|
250 |
+
return input_ids
|
251 |
+
|
252 |
+
|
253 |
+
def to_mel_to_pad(array):
|
254 |
+
"""Static function which:
|
255 |
+
1. Computes log-mel filter coefficients from padded/trimmed audio sequences
|
256 |
+
2. Pads/trims a list of audio arrays to a max length of 30s
|
257 |
+
Inputs:
|
258 |
+
array: list of audio arrays
|
259 |
+
Returns:
|
260 |
+
input_ids: torch.tensor of log-mel filter bank coefficients
|
261 |
+
"""
|
262 |
+
mels = whisper.log_mel_spectrogram(np.asarray(array, dtype=np.float32))
|
263 |
+
input_ids = whisper.pad_or_trim(mels, 3000)
|
264 |
+
return input_ids
|
265 |
+
|
266 |
+
|
267 |
+
@dataclass
|
268 |
+
class WhisperDataCollatorWithPadding:
|
269 |
+
"""
|
270 |
+
Data collator that dynamically pads the audio inputs received. An EOS token is appended to the labels sequences.
|
271 |
+
They are then dynamically padded to max length.
|
272 |
+
Args:
|
273 |
+
eos_token_id (`int`)
|
274 |
+
The end-of-sentence token for the Whisper tokenizer. Ensure to set for sequences to terminate before
|
275 |
+
generation max length.
|
276 |
+
"""
|
277 |
+
|
278 |
+
eos_token_id: int
|
279 |
+
|
280 |
+
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
|
281 |
+
"""
|
282 |
+
Since Whisper models don't have a HF processor defined (feature extractor + tokenizer), we'll pad by hand...
|
283 |
+
"""
|
284 |
+
# split inputs and labels since they have to be of different lengths
|
285 |
+
# and need different padding methods
|
286 |
+
input_ids = [feature["input_ids"] for feature in features]
|
287 |
+
labels = [feature["labels"] for feature in features]
|
288 |
+
|
289 |
+
# first, pad the audio inputs to max_len
|
290 |
+
input_ids = torch.concat([to_pad_to_mel(input_val)[None, :] for input_val in input_ids])
|
291 |
+
|
292 |
+
# next, append the eos token to our sequence of labels
|
293 |
+
labels = [lab + [self.eos_token_id] for lab in labels]
|
294 |
+
# finally, pad the target labels to max_len
|
295 |
+
label_lengths = [len(lab) for lab in labels]
|
296 |
+
max_label_len = max(label_lengths)
|
297 |
+
labels = [np.pad(lab, (0, max_label_len - lab_len), 'constant', constant_values=-100) for lab, lab_len in zip(labels, label_lengths)]
|
298 |
+
|
299 |
+
batch = {"labels": labels}
|
300 |
+
batch = {k: torch.tensor(np.array(v), requires_grad=False) for k, v in batch.items()}
|
301 |
+
|
302 |
+
batch["input_ids"] = input_ids
|
303 |
+
|
304 |
+
return batch
|
305 |
+
|
306 |
+
|
307 |
+
def main():
|
308 |
+
# See all possible arguments in src/transformers/training_args.py
|
309 |
+
# or by passing the --help flag to this script.
|
310 |
+
# We now keep distinct sets of args, for a cleaner separation of concerns.
|
311 |
+
|
312 |
+
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
|
313 |
+
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
|
314 |
+
# If we pass only one argument to the script and it's the path to a json file,
|
315 |
+
# let's parse it to get our arguments.
|
316 |
+
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
|
317 |
+
else:
|
318 |
+
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
|
319 |
+
|
320 |
+
# Set wandb project ID before instantiating the Trainer
|
321 |
+
os.environ["WANDB_PROJECT"] = data_args.wandb_project
|
322 |
+
report_to_wandb = "wandb" in training_args.report_to
|
323 |
+
|
324 |
+
sample_rate = 16_000
|
325 |
+
|
326 |
+
# Detecting last checkpoint.
|
327 |
+
last_checkpoint = None
|
328 |
+
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
|
329 |
+
last_checkpoint = get_last_checkpoint(training_args.output_dir)
|
330 |
+
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
|
331 |
+
raise ValueError(
|
332 |
+
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
|
333 |
+
"Use --overwrite_output_dir to overcome."
|
334 |
+
)
|
335 |
+
elif last_checkpoint is not None:
|
336 |
+
logger.info(
|
337 |
+
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
|
338 |
+
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
|
339 |
+
)
|
340 |
+
|
341 |
+
# Setup logging
|
342 |
+
logging.basicConfig(
|
343 |
+
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
|
344 |
+
datefmt="%m/%d/%Y %H:%M:%S",
|
345 |
+
handlers=[logging.StreamHandler(sys.stdout)],
|
346 |
+
)
|
347 |
+
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
|
348 |
+
|
349 |
+
# Log on each process the small summary:
|
350 |
+
logger.warning(
|
351 |
+
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
|
352 |
+
f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
|
353 |
+
)
|
354 |
+
# Set the verbosity to info of the Transformers logger (on main process only):
|
355 |
+
if is_main_process(training_args.local_rank):
|
356 |
+
transformers.utils.logging.set_verbosity_info()
|
357 |
+
logger.info("Training/evaluation parameters %s", training_args)
|
358 |
+
|
359 |
+
# Set seed before initializing model.
|
360 |
+
set_seed(training_args.seed)
|
361 |
+
|
362 |
+
# load the model
|
363 |
+
if os.path.isfile(model_args.model_name_or_path):
|
364 |
+
checkpoint = torch.load(model_args.model_name_or_path)
|
365 |
+
need_to_rewrite_checkpoint = any(k.startswith("decoder.blocks") and ".mlp.3" in k for k in checkpoint.keys())
|
366 |
+
if need_to_rewrite_checkpoint:
|
367 |
+
new_checkpoint = {}
|
368 |
+
for k, v in checkpoint.items():
|
369 |
+
if k.startswith("decoder.blocks") and "mlp" in k.split("."):
|
370 |
+
if int(k.split(".mlp.")[-1].split(".")[0]) in [2, 4]:
|
371 |
+
continue
|
372 |
+
elif int(k.split(".mlp.")[-1].split(".")[0]) == 3:
|
373 |
+
k = k.replace(".mlp.3", ".mlp.2")
|
374 |
+
|
375 |
+
new_checkpoint[k] = v
|
376 |
+
|
377 |
+
with tempfile.TemporaryDirectory() as tmp:
|
378 |
+
file = os.path.join(tmp, "model.pt")
|
379 |
+
torch.save(new_checkpoint, file)
|
380 |
+
model = whisper.Whisper.load_trained(file)
|
381 |
+
else:
|
382 |
+
model = whisper.Whisper.load_trained(model_args.model_name_or_path)
|
383 |
+
del checkpoint
|
384 |
+
else:
|
385 |
+
model = whisper.load_model(model_args.model_name_or_path, dropout_rate=model_args.dropout_rate)
|
386 |
+
|
387 |
+
if training_args.do_train:
|
388 |
+
# set the dropout for the MLP layers -> we do this here as the MLP layers are written as a 'sequential'
|
389 |
+
# so changing the modelling code gives mis-matches in the state-dict
|
390 |
+
if not model_args.freeze_encoder:
|
391 |
+
# only apply dropout when training the encoder
|
392 |
+
for block_idx in range(len(model.encoder.blocks)):
|
393 |
+
mlp_layer = model.encoder.blocks[block_idx].mlp
|
394 |
+
# going very verbose to explain what we're doing here!
|
395 |
+
fc1 = mlp_layer[0]
|
396 |
+
act_fn = mlp_layer[1]
|
397 |
+
dropout = nn.Dropout(p=model_args.dropout_rate)
|
398 |
+
fc2 = mlp_layer[2]
|
399 |
+
model.encoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout, fc2, dropout)
|
400 |
+
|
401 |
+
for block_idx in range(len(model.decoder.blocks)):
|
402 |
+
mlp_layer = model.decoder.blocks[block_idx].mlp
|
403 |
+
fc1 = mlp_layer[0]
|
404 |
+
act_fn = mlp_layer[1]
|
405 |
+
dropout_1 = nn.Dropout(p=model_args.dropout_rate)
|
406 |
+
fc2 = mlp_layer[2]
|
407 |
+
dropout_2 = nn.Dropout(p=model_args.dropout_rate)
|
408 |
+
model.decoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout_1, fc2, dropout_2)
|
409 |
+
for block_idx in range(len(model.decoder.blocks)):
|
410 |
+
mlp_layer = model.decoder.blocks[block_idx].mlp
|
411 |
+
fc1 = mlp_layer[0]
|
412 |
+
act_fn = mlp_layer[1]
|
413 |
+
dropout1 = nn.Dropout(p=model_args.dropout_rate)
|
414 |
+
fc2 = mlp_layer[2]
|
415 |
+
dropout2 = nn.Dropout(p=model_args.dropout_rate)
|
416 |
+
model.decoder.blocks[block_idx].mlp = nn.Sequential(fc1, act_fn, dropout1, fc2, dropout2)
|
417 |
+
|
418 |
+
# load the tokenizer
|
419 |
+
whisper_tok = whisper.tokenizer.get_tokenizer(False, task="transcribe", language="en")
|
420 |
+
tokenizer = whisper_tok.tokenizer
|
421 |
+
tokenizer.pad_token = tokenizer.eos_token
|
422 |
+
|
423 |
+
# 4. Load dataset
|
424 |
+
raw_datasets = DatasetDict()
|
425 |
+
|
426 |
+
if training_args.do_train:
|
427 |
+
raw_datasets["train"] = load_dataset(
|
428 |
+
data_args.dataset_name,
|
429 |
+
data_args.dataset_config_name,
|
430 |
+
split=data_args.train_split_name,
|
431 |
+
cache_dir=data_args.dataset_cache_dir,
|
432 |
+
use_auth_token=True if model_args.use_auth_token else None,
|
433 |
+
)
|
434 |
+
|
435 |
+
if training_args.do_eval:
|
436 |
+
raw_datasets["eval"] = load_dataset(
|
437 |
+
data_args.dataset_name,
|
438 |
+
data_args.dataset_config_name,
|
439 |
+
split=data_args.eval_split_name,
|
440 |
+
cache_dir=data_args.dataset_cache_dir,
|
441 |
+
use_auth_token=True if model_args.use_auth_token else None,
|
442 |
+
)
|
443 |
+
|
444 |
+
if training_args.do_predict:
|
445 |
+
test_split = data_args.test_split_name.split("+")
|
446 |
+
for split in test_split:
|
447 |
+
raw_datasets[split] = load_dataset(
|
448 |
+
data_args.dataset_name,
|
449 |
+
data_args.dataset_config_name,
|
450 |
+
split=split,
|
451 |
+
cache_dir=data_args.dataset_cache_dir,
|
452 |
+
use_auth_token=True if model_args.use_auth_token else None,
|
453 |
+
)
|
454 |
+
|
455 |
+
if not training_args.do_train and not training_args.do_eval and not training_args.do_predict:
|
456 |
+
raise ValueError(
|
457 |
+
"Cannot not train, not do evaluation and not do prediction. At least one of "
|
458 |
+
"training, evaluation or prediction has to be done."
|
459 |
+
)
|
460 |
+
|
461 |
+
# if not training, there is no need to run multiple epochs
|
462 |
+
if not training_args.do_train:
|
463 |
+
training_args.num_train_epochs = 1
|
464 |
+
|
465 |
+
if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
|
466 |
+
raise ValueError(
|
467 |
+
f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
|
468 |
+
"Make sure to set `--audio_column_name` to the correct audio column - one of "
|
469 |
+
f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
|
470 |
+
)
|
471 |
+
|
472 |
+
if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
|
473 |
+
raise ValueError(
|
474 |
+
f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
|
475 |
+
"Make sure to set `--text_column_name` to the correct text column - one of "
|
476 |
+
f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
|
477 |
+
)
|
478 |
+
|
479 |
+
# 6. Resample speech dataset ALWAYS
|
480 |
+
raw_datasets = raw_datasets.cast_column(
|
481 |
+
data_args.audio_column_name, datasets.features.Audio(sampling_rate=sample_rate)
|
482 |
+
)
|
483 |
+
|
484 |
+
# 7. Preprocessing the datasets.
|
485 |
+
# We need to read the audio files as arrays and tokenize the targets.
|
486 |
+
max_input_length = int(data_args.max_duration_in_seconds * sample_rate)
|
487 |
+
min_input_length = min(int(data_args.min_duration_in_seconds * sample_rate), 1)
|
488 |
+
max_eval_input_length = int(data_args.max_eval_duration_in_seconds * sample_rate) if data_args.max_eval_duration_in_seconds else None
|
489 |
+
max_target_length = data_args.max_target_length
|
490 |
+
min_target_length = data_args.min_target_length
|
491 |
+
audio_column_name = data_args.audio_column_name
|
492 |
+
num_workers = data_args.preprocessing_num_workers
|
493 |
+
text_column_name = data_args.text_column_name
|
494 |
+
|
495 |
+
if training_args.do_train and data_args.max_train_samples is not None:
|
496 |
+
raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
|
497 |
+
|
498 |
+
if training_args.do_eval and data_args.max_eval_samples is not None:
|
499 |
+
raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_eval_samples))
|
500 |
+
|
501 |
+
if training_args.do_predict and data_args.max_predict_samples is not None:
|
502 |
+
for split in test_split:
|
503 |
+
raw_datasets[split] = raw_datasets[split].select(range(data_args.max_predict_samples))
|
504 |
+
|
505 |
+
|
506 |
+
def prepare_dataset(batch):
|
507 |
+
# pre-process audio
|
508 |
+
sample = batch[audio_column_name]
|
509 |
+
|
510 |
+
# For training Whisper we perform the audio preprocessing in the WhisperDataCollator
|
511 |
+
# => we only need to supply it with the raw audio values
|
512 |
+
batch["input_ids"] = sample["array"]
|
513 |
+
batch["input_lengths"] = len(batch["input_ids"])
|
514 |
+
|
515 |
+
input_str = batch[text_column_name]
|
516 |
+
batch["labels"] = tokenizer(input_str).input_ids
|
517 |
+
return batch
|
518 |
+
|
519 |
+
vectorized_datasets = raw_datasets.map(
|
520 |
+
prepare_dataset,
|
521 |
+
remove_columns=next(iter(raw_datasets.values())).column_names,
|
522 |
+
num_proc=num_workers,
|
523 |
+
desc="preprocess train dataset",
|
524 |
+
)
|
525 |
+
|
526 |
+
# filter training data with inputs longer than max_input_length
|
527 |
+
def is_audio_in_length_range(input_length):
|
528 |
+
return min_input_length < input_length < max_input_length
|
529 |
+
|
530 |
+
if training_args.do_train:
|
531 |
+
vectorized_datasets["train"] = vectorized_datasets["train"].filter(
|
532 |
+
is_audio_in_length_range,
|
533 |
+
num_proc=num_workers,
|
534 |
+
input_columns=["input_lengths"],
|
535 |
+
)
|
536 |
+
|
537 |
+
if max_eval_input_length is not None:
|
538 |
+
# filter training data with inputs longer than max_input_length
|
539 |
+
def is_eval_audio_in_length_range(input_length):
|
540 |
+
return min_input_length < input_length < max_eval_input_length
|
541 |
+
|
542 |
+
if training_args.do_eval:
|
543 |
+
vectorized_datasets["eval"] = vectorized_datasets["eval"].filter(
|
544 |
+
is_eval_audio_in_length_range,
|
545 |
+
num_proc=num_workers,
|
546 |
+
input_columns=["input_lengths"],
|
547 |
+
)
|
548 |
+
|
549 |
+
if training_args.do_predict:
|
550 |
+
for split in test_split:
|
551 |
+
vectorized_datasets[split] = vectorized_datasets[split].filter(
|
552 |
+
is_eval_audio_in_length_range,
|
553 |
+
num_proc=num_workers,
|
554 |
+
input_columns=["input_lengths"],
|
555 |
+
)
|
556 |
+
|
557 |
+
# filter training data with targets shorter than min_target_length or longer than max_target_length
|
558 |
+
def is_labels_in_length_range(labels):
|
559 |
+
return min_target_length < len(labels) < max_target_length
|
560 |
+
|
561 |
+
if training_args.do_train:
|
562 |
+
vectorized_datasets["train"] = vectorized_datasets["train"].filter(
|
563 |
+
is_labels_in_length_range,
|
564 |
+
num_proc=num_workers,
|
565 |
+
input_columns=["labels"],
|
566 |
+
)
|
567 |
+
|
568 |
+
# for large datasets it is advised to run the preprocessing on a
|
569 |
+
# single machine first with `args.preprocessing_only` since there will mostly likely
|
570 |
+
# be a timeout when running the script in distributed mode.
|
571 |
+
# In a second step `args.preprocessing_only` can then be set to `False` to load the
|
572 |
+
# cached dataset
|
573 |
+
if data_args.preprocessing_only:
|
574 |
+
cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
|
575 |
+
logger.info(f"Data preprocessing finished. Files cached at {cache}.")
|
576 |
+
return
|
577 |
+
|
578 |
+
if model_args.freeze_encoder:
|
579 |
+
model.freeze_encoder()
|
580 |
+
logging.info("Model encoder has been frozen")
|
581 |
+
|
582 |
+
# 8. Load Metric
|
583 |
+
metric_wer = datasets.load_metric("wer")
|
584 |
+
metric_cer = datasets.load_metric("cer")
|
585 |
+
|
586 |
+
def compute_metrics(pred):
|
587 |
+
pred_ids = pred.predictions
|
588 |
+
pred.label_ids[pred.label_ids == -100] = tokenizer.eos_token_id
|
589 |
+
|
590 |
+
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
|
591 |
+
pred_str = [x.lstrip().strip() for x in pred_str]
|
592 |
+
|
593 |
+
# we do not want to group tokens when computing the metrics
|
594 |
+
label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
|
595 |
+
|
596 |
+
wer = metric_wer.compute(predictions=pred_str, references=label_str)
|
597 |
+
cer = metric_cer.compute(predictions=pred_str, references=label_str)
|
598 |
+
|
599 |
+
return {"wer": wer, "cer": cer}
|
600 |
+
|
601 |
+
def compute_metrics_and_predictions(pred):
|
602 |
+
pred_ids = pred.predictions
|
603 |
+
pred.label_ids[pred.label_ids == -100] = tokenizer.eos_token_id
|
604 |
+
|
605 |
+
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
|
606 |
+
pred_str = [x.lstrip().strip() for x in pred_str]
|
607 |
+
|
608 |
+
# we do not want to group tokens when computing the metrics
|
609 |
+
label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
|
610 |
+
|
611 |
+
wer = metric_wer.compute(predictions=pred_str, references=label_str)
|
612 |
+
cer = metric_cer.compute(predictions=pred_str, references=label_str)
|
613 |
+
|
614 |
+
return {"wer": wer, "cer": cer, "pred_str": pred_str, "label_str": label_str}
|
615 |
+
|
616 |
+
class WhisperTrainer(Seq2SeqTrainer):
|
617 |
+
def _save(self, output_dir: Optional[str] = None, state_dict=None):
|
618 |
+
# If we are executing this function, we are the process zero, so we don't check for that.
|
619 |
+
output_dir = output_dir if output_dir is not None else self.args.output_dir
|
620 |
+
os.makedirs(output_dir, exist_ok=True)
|
621 |
+
logger.info(f"Saving model checkpoint to {output_dir}")
|
622 |
+
# Save a trained model and configuration using `save_pretrained()`.
|
623 |
+
# They can then be reloaded using `from_pretrained()`
|
624 |
+
self.model.save_to(save_path=os.path.join(output_dir, model_args.model_name_or_path + ".whisper"))
|
625 |
+
# Good practice: save your training arguments together with the trained model
|
626 |
+
torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
|
627 |
+
|
628 |
+
# Define data collator
|
629 |
+
whisper_data_collator = WhisperDataCollatorWithPadding(eos_token_id=tokenizer.eos_token_id)
|
630 |
+
|
631 |
+
# make sure model uses 50257 as BOS
|
632 |
+
bos = tokenizer("<|startoftranscript|>").input_ids[0]
|
633 |
+
model.config.decoder_start_token_id = bos
|
634 |
+
|
635 |
+
# Initialize Trainer
|
636 |
+
trainer = WhisperTrainer(
|
637 |
+
model=model,
|
638 |
+
args=training_args,
|
639 |
+
compute_metrics=compute_metrics,
|
640 |
+
train_dataset=vectorized_datasets['train'] if training_args.do_train else None,
|
641 |
+
eval_dataset=vectorized_datasets['eval'] if training_args.do_eval else None,
|
642 |
+
data_collator=whisper_data_collator,
|
643 |
+
)
|
644 |
+
|
645 |
+
# 8. Finally, we can start training
|
646 |
+
|
647 |
+
# Training
|
648 |
+
if training_args.do_train:
|
649 |
+
|
650 |
+
# use last checkpoint if exist
|
651 |
+
if last_checkpoint is not None:
|
652 |
+
checkpoint = last_checkpoint
|
653 |
+
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path):
|
654 |
+
checkpoint = model_args.model_name_or_path
|
655 |
+
else:
|
656 |
+
checkpoint = None
|
657 |
+
|
658 |
+
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
659 |
+
trainer.save_model()
|
660 |
+
|
661 |
+
metrics = train_result.metrics
|
662 |
+
max_train_samples = (
|
663 |
+
data_args.max_train_samples
|
664 |
+
if data_args.max_train_samples is not None
|
665 |
+
else len(vectorized_datasets["train"])
|
666 |
+
)
|
667 |
+
metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
|
668 |
+
|
669 |
+
trainer.log_metrics("train", metrics)
|
670 |
+
trainer.save_metrics("train", metrics)
|
671 |
+
trainer.save_state()
|
672 |
+
|
673 |
+
# Change decoding strategy for final eval/predict
|
674 |
+
# if training_args.do_eval or training_args.do_predict:
|
675 |
+
# trainer.model.num_beams = 2
|
676 |
+
|
677 |
+
trainer.compute_metrics = compute_metrics_and_predictions
|
678 |
+
|
679 |
+
results = {}
|
680 |
+
if training_args.do_eval:
|
681 |
+
if not training_args.do_train and report_to_wandb:
|
682 |
+
# manually init wandb
|
683 |
+
wandb.init(project=data_args.wandb_project, name=training_args.run_name)
|
684 |
+
# Have to run this as a predict step, otherwise trainer will try to log the pred/label strings to wandb
|
685 |
+
eval_results = trainer.predict(vectorized_datasets["eval"], metric_key_prefix="eval", num_beams=model_args.num_beams, length_penalty=model_args.length_penalty)
|
686 |
+
metrics = eval_results.metrics
|
687 |
+
max_eval_samples = (
|
688 |
+
data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
|
689 |
+
)
|
690 |
+
metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
|
691 |
+
pred_str = metrics.pop("eval_pred_str", None)
|
692 |
+
label_str = metrics.pop("eval_label_str", None)
|
693 |
+
|
694 |
+
trainer.log_metrics("eval", metrics)
|
695 |
+
trainer.save_metrics("eval", metrics)
|
696 |
+
|
697 |
+
if report_to_wandb:
|
698 |
+
metrics = {os.path.join("eval", k[len("eval") + 1:]): v for k, v in metrics.items()}
|
699 |
+
wandb.log(metrics)
|
700 |
+
write_wandb_pred(pred_str, label_str, prefix="eval")
|
701 |
+
|
702 |
+
if training_args.do_predict:
|
703 |
+
if not training_args.do_train and not training_args.do_eval and report_to_wandb:
|
704 |
+
# manually init wandb
|
705 |
+
wandb.init(project=data_args.wandb_project, name=training_args.run_name)
|
706 |
+
for split in test_split:
|
707 |
+
predict_results = trainer.predict(
|
708 |
+
vectorized_datasets[split], metric_key_prefix=split, num_beams=model_args.num_beams, length_penalty=model_args.length_penalty)
|
709 |
+
metrics = predict_results.metrics
|
710 |
+
max_predict_samples = (
|
711 |
+
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(vectorized_datasets[split])
|
712 |
+
)
|
713 |
+
metrics[f"{split}_samples"] = min(max_predict_samples, len(vectorized_datasets[split]))
|
714 |
+
pred_str = metrics.pop(f"{split}_pred_str", None)
|
715 |
+
label_str = metrics.pop(f"{split}_label_str", None)
|
716 |
+
|
717 |
+
trainer.log_metrics(split, metrics)
|
718 |
+
trainer.save_metrics(split, metrics)
|
719 |
+
|
720 |
+
if report_to_wandb:
|
721 |
+
metrics = {os.path.join(split, k[len(split)+1:]): v for k, v in metrics.items()}
|
722 |
+
wandb.log(metrics)
|
723 |
+
write_wandb_pred(pred_str, label_str, prefix=split)
|
724 |
+
|
725 |
+
# Write model card and (optionally) push to hub
|
726 |
+
config_name = data_args.dataset_config_name if data_args.dataset_config_name is not None else "na"
|
727 |
+
kwargs = {
|
728 |
+
"finetuned_from": model_args.model_name_or_path,
|
729 |
+
"tasks": "speech-recognition",
|
730 |
+
"tags": ["automatic-speech-recognition", data_args.dataset_name],
|
731 |
+
"dataset_args": (
|
732 |
+
f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split:"
|
733 |
+
f" {data_args.eval_split_name}"
|
734 |
+
),
|
735 |
+
"dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
|
736 |
+
}
|
737 |
+
if "common_voice" in data_args.dataset_name:
|
738 |
+
kwargs["language"] = config_name
|
739 |
+
|
740 |
+
if training_args.push_to_hub:
|
741 |
+
trainer.push_to_hub(**kwargs)
|
742 |
+
|
743 |
+
return results
|
744 |
+
|
745 |
+
|
746 |
+
if __name__ == "__main__":
|
747 |
+
main()
|
run_switchboard.sh
ADDED
@@ -0,0 +1,35 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env bash
|
2 |
+
CUDA_VISIBLE_DEVICES=0 python run_speech_recognition_whisper.py \
|
3 |
+
--model_name_or_path="medium.en" \
|
4 |
+
--dataset_name="esc-benchmark/esc-datasets" \
|
5 |
+
--dataset_config_name="switchboard" \
|
6 |
+
--max_steps="5000" \
|
7 |
+
--output_dir="./" \
|
8 |
+
--run_name="whisper-switchboard" \
|
9 |
+
--max_steps="5000" \
|
10 |
+
--output_dir="./" \
|
11 |
+
--run_name="whisper-switchboard" \
|
12 |
+
--wandb_project="whisper" \
|
13 |
+
--per_device_train_batch_size="64" \
|
14 |
+
--per_device_eval_batch_size="16" \
|
15 |
+
--logging_steps="25" \
|
16 |
+
--learning_rate="1e-4" \
|
17 |
+
--warmup_steps="500" \
|
18 |
+
--report_to="wandb" \
|
19 |
+
--preprocessing_num_workers="16" \
|
20 |
+
--evaluation_strategy="steps" \
|
21 |
+
--eval_steps="1000" \
|
22 |
+
--save_strategy="steps" \
|
23 |
+
--save_steps="1000" \
|
24 |
+
--generation_max_length="224" \
|
25 |
+
--length_column_name="input_lengths" \
|
26 |
+
--gradient_checkpointing \
|
27 |
+
--group_by_length \
|
28 |
+
--freeze_encoder \
|
29 |
+
--fp16 \
|
30 |
+
--overwrite_output_dir \
|
31 |
+
--do_train \
|
32 |
+
--do_eval \
|
33 |
+
--do_predict \
|
34 |
+
--predict_with_generate \
|
35 |
+
--use_auth_token
|