speech-recognition

Automatic Speech Recognition examples

Connectionist Temporal Classification without Language Model (CTC w/o LM)

The script run_speech_recognition_ctc.py can be used to fine-tune any pretrained Connectionist Temporal Classification Model for automatic speech recognition on one of the official speech recognition datasets or a custom dataset.

Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, e.g. Wav2Vec2, HuBERT, XLSR-Wav2Vec2, have shown to require only very little annotated data to yield good performance on automatic speech recognition datasets.

In the script [run_speech_recognition_ctc], we first create a vocabulary from all unique characters of both the training data and evaluation data. Then, we preprocesses the speech recognition dataset, which includes correct resampling, normalization and padding. Finally, the pretrained speech recognition model is fine-tuned on the annotated speech recognition datasets using CTC loss.

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NOTE

If you encounter problems with data preprocessing by setting --preprocessing_num_workers > 1, you might want to set the environment variable OMP_NUM_THREADS to 1 as follows:

OMP_NUM_THREADS=1 python run_speech_recognition_ctc ...

If the environment variable is not set, the training script might freeze, i.e. see: pytorch/audio#1021 (comment)

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Single-GPU

The following command shows how to fine-tune XLSR-Wav2Vec2 on Common Voice using a single GPU in half-precision.

python run_speech_recognition_ctc.py \
	--dataset_name="common_voice" \
	--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
	--dataset_config_name="tr" \
	--output_dir="./wav2vec2-common_voice-tr-demo" \
	--overwrite_output_dir \
	--num_train_epochs="15" \
	--per_device_train_batch_size="16" \
	--gradient_accumulation_steps="2" \
	--learning_rate="3e-4" \
	--warmup_steps="500" \
	--evaluation_strategy="steps" \
	--text_column_name="sentence" \
	--save_steps="400" \
	--eval_steps="100" \
	--layerdrop="0.0" \
	--save_total_limit="3" \
	--freeze_feature_extractor \
	--gradient_checkpointing \
	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
	--fp16 \
	--group_by_length \
	--push_to_hub \
	--do_train --do_eval 

On a single V100 GPU, this script should run in ca. 1 hour 20 minutes and yield a CTC loss of 0.39 and word error rate of 0.35.

Multi-GPU

The following command shows how to fine-tune XLSR-Wav2Vec2 on Common Voice using 8 GPUs in half-precision.

python -m torch.distributed.launch \
	--nproc_per_node 8 run_speech_recognition_ctc.py \
	--dataset_name="common_voice" \
	--model_name_or_path="facebook/wav2vec2-large-xlsr-53" \
	--dataset_config_name="tr" \
	--output_dir="./wav2vec2-common_voice-tr-demo-dist" \
	--overwrite_output_dir \
	--num_train_epochs="15" \
	--per_device_train_batch_size="4" \
	--learning_rate="3e-4" \
	--warmup_steps="500" \
	--evaluation_strategy="steps" \
	--audio_column_name="path" \
	--text_column_name="sentence" \
	--save_steps="400" \
	--eval_steps="100" \
	--logging_steps="1" \
	--layerdrop="0.0" \
	--save_total_limit="3" \
	--freeze_feature_extractor \
	--gradient_checkpointing \
	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
	--fp16 \
	--group_by_length \
	--push_to_hub \
	--do_train --do_eval

On 8 V100 GPUs, this script should run in ca. 18 minutes and yield a CTC loss of 0.39 and word error rate of 0.36.

Examples

In the following a couple of demonstration fine-tuning runs are listed. It has been verified that the script works for the following datasets:

• Common Voice
• Librispeech

Dataset	Dataset Config	Pretrained Model	Word error rate on eval	GPU setup	Training time	Fine-tuned Model & Logs
Librispeech	"clean" - "train.100"	facebook/wav2vec2-large-lv60	0.042	8 GPU V100	1h30min	here
Librispeech	"clean" - "train.100"	facebook/hubert-large-ll60k	0.088	8 GPU V100	1h30min	here
Common Voice	"tr"	facebook/wav2vec2-large-xlsr-53	0.36	8 GPU V100	18min	here
Common Voice	"tr"	facebook/wav2vec2-large-xlsr-53	0.35	1 GPU V100	1h20min	here