[Flax Examples] Seq2Seq ASR Fine-Tuning Script

examples/flax/_tests_requirements.txt

@@ -5,4 +5,6 @@ nltk
 rouge-score
 seqeval
 tensorboard
-evaluate >= 0.2.0
+evaluate >= 0.2.0
+torch
+accelerate

examples/flax/speech-recognition/README.md

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+<!---
+Copyright 2023 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.
+-->
+
+# Automatic Speech Recognition - Flax Examples
+
+## Sequence to Sequence
+
+The script [`run_flax_speech_recognition_seq2seq.py`](https://github.com/huggingface/transformers/blob/main/examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py) 
+can be used to fine-tune any [Flax Speech Sequence-to-Sequence Model](https://huggingface.co/docs/transformers/main/en/model_doc/auto#transformers.FlaxAutoModelForSpeechSeq2Seq) 
+for automatic speech recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) 
+or a custom dataset. This includes the Whisper model from OpenAI, or a warm-started Speech-Encoder-Decoder Model, 
+an example for which is included below.
+
+### Whisper Model
+
+We can load all components of the Whisper model directly from the pretrained checkpoint, including the pretrained model 
+weights, feature extractor and tokenizer. We simply have to specify the id of fine-tuning dataset and the necessary
+training hyperparameters.
+
+The following example shows how to fine-tune the [Whisper small](https://huggingface.co/openai/whisper-small) checkpoint 
+on the Hindi subset of the [Common Voice 13](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) dataset.
+Note that before running this script you must accept the dataset's [terms of use](https://huggingface.co/datasets/mozilla-foundation/common_voice_13_0) 
+and register your Hugging Face Hub token on your device by running `huggingface-hub login`.
+
+```bash
+python run_flax_speech_recognition_seq2seq.py \
+	--model_name_or_path="openai/whisper-small" \
+	--dataset_name="mozilla-foundation/common_voice_13_0" \
+	--dataset_config_name="hi" \
+	--language="hindi" \
+	--train_split_name="train+validation" \
+	--eval_split_name="test" \
+	--output_dir="./whisper-small-hi-flax" \
+	--per_device_train_batch_size="16" \
+	--per_device_eval_batch_size="16" \
+	--num_train_epochs="10" \
+	--learning_rate="1e-4" \
+	--warmup_steps="500" \
+	--logging_steps="25" \
+	--generation_max_length="40" \
+	--preprocessing_num_workers="32" \
+	--dataloader_num_workers="32" \
+	--max_duration_in_seconds="30" \
+	--text_column_name="sentence" \
+	--overwrite_output_dir \
+	--do_train \
+	--do_eval \
+	--predict_with_generate \
+	--push_to_hub \
+	--use_auth_token
+```
+
+On a TPU v4-8, training should take approximately 25 minutes, with a final cross-entropy loss of 0.02 and word error 
+rate of **34%**. See the checkpoint [sanchit-gandhi/whisper-small-hi-flax](https://huggingface.co/sanchit-gandhi/whisper-small-hi-flax)
+for an example training run.

examples/flax/speech-recognition/requirements.txt

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+datasets[audio]>=2.14.0
+jax>=0.3.6
+jaxlib>=0.3.6
+flax>=0.4.1
+optax>=0.0.8
+torch>=1.9.0
+jiwer
+evaluate