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README.md

Automatic Speech Recognition Examples

Table of Contents

Connectionist Temporal Classification

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.

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)

Single GPU CTC

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" \
	--length_column_name="input_length" \
	--save_steps="400" \
	--eval_steps="100" \
	--layerdrop="0.0" \
	--save_total_limit="3" \
	--freeze_feature_encoder \
	--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 CTC

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" \
	--text_column_name="sentence" \
	--length_column_name="input_length" \
	--save_steps="400" \
	--eval_steps="100" \
	--logging_steps="1" \
	--layerdrop="0.0" \
	--save_total_limit="3" \
	--freeze_feature_encoder \
	--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.

Multi GPU CTC with Dataset Streaming

The following command shows how to use Dataset Streaming mode to fine-tune XLS-R on Common Voice using 4 GPUs in half-precision.

Streaming mode imposes several constraints on training:

  1. We need to construct a tokenizer beforehand and define it via --tokenizer_name_or_path.
  2. --num_train_epochs has to be replaced by --max_steps. Similarly, all other epoch-based arguments have to be replaced by step-based ones.
  3. Full dataset shuffling on each epoch is not possible, since we don't have the whole dataset available at once. However, the --shuffle_buffer_size argument controls how many examples we can pre-download before shuffling them.
**python -m torch.distributed.launch \
	--nproc_per_node 4 run_speech_recognition_ctc_streaming.py \
	--dataset_name="common_voice" \
	--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
	--tokenizer_name_or_path="anton-l/wav2vec2-tokenizer-turkish" \
	--dataset_config_name="tr" \
	--train_split_name="train+validation" \
	--eval_split_name="test" \
	--output_dir="wav2vec2-xls-r-common_voice-tr-ft" \
	--overwrite_output_dir \
	--max_steps="5000" \
	--per_device_train_batch_size="8" \
	--gradient_accumulation_steps="2" \
	--learning_rate="5e-4" \
	--warmup_steps="500" \
	--evaluation_strategy="steps" \
	--text_column_name="sentence" \
	--save_steps="500" \
	--eval_steps="500" \
	--logging_steps="1" \
	--layerdrop="0.0" \
	--eval_metrics wer cer \
	--save_total_limit="1" \
	--mask_time_prob="0.3" \
	--mask_time_length="10" \
	--mask_feature_prob="0.1" \
	--mask_feature_length="64" \
	--freeze_feature_encoder \
	--chars_to_ignore , ? . ! - \; \: \" “ % ‘ ” � \
	--max_duration_in_seconds="20" \
	--shuffle_buffer_size="500" \
	--fp16 \
	--push_to_hub \
	--do_train --do_eval \
	--gradient_checkpointing**

On 4 V100 GPUs, this script should run in ca. 3h 31min and yield a CTC loss of 0.35 and word error rate of 0.29.

Examples CTC

The following tables present a couple of example runs on the most popular speech-recognition datasets. The presented performances are by no means optimal as no hyper-parameter tuning was done. Nevertheless, they can serve as a baseline to improve upon.

TIMIT CTC

Dataset Dataset Config Pretrained Model Word error rate on eval Phoneme error rate on eval GPU setup Training time Fine-tuned Model & Logs Command to reproduce
TIMIT - wav2vec2-base 0.21 - 1 GPU TITAN RTX 32min here run.sh
TIMIT - wav2vec2-base 0.21 - 1 GPU TITAN RTX 32min here run.sh
TIMIT - unispeech-large-1500h-cv 0.22 - 1 GPU TITAN RTX 35min here run.sh
TIMIT - asapp/sew-mid-100k 0.30 - 1 GPU TITAN RTX 28min here run.sh
TIMIT - ntu-spml/distilhubert 0.68 - 1 GPU TITAN RTX 26min here run.sh

Librispeech CTC

Dataset Dataset Config Pretrained Model Word error rate on eval Phoneme error rate on eval GPU setup Training time Fine-tuned Model & Logs Command to reproduce
Librispeech "clean" - "train.100" microsoft/wavlm-large 0.049 - 8 GPU V100 1h30min here run.sh
Librispeech "clean" - "train.100" microsoft/wavlm-base-plus 0.068 - 8 GPU V100 1h30min here run.sh
Librispeech "clean" - "train.100" facebook/wav2vec2-large-lv60 0.042 - 8 GPU V100 1h30min here run.sh
Librispeech "clean" - "train.100" facebook/wav2vec2-large-lv60 0.042 - 8 GPU V100 1h30min here run.sh
Librispeech "clean" - "train.100" facebook/hubert-large-ll60k 0.088 - 8 GPU V100 1h30min here run.sh
Librispeech "clean" - "train.100" asapp/sew-mid-100k 0.167 8 GPU V100 54min here run.sh

Common Voice CTC

Dataset Dataset Config Pretrained Model Word error rate on eval Phoneme error rate on eval GPU setup Training time Fine-tuned Model & Logs Command to reproduce
Common Voice "tr" facebook/wav2vec2-large-xls-r-300m - 0.099 8 GPU V100 23min here run.sh
Common Voice "it" facebook/wav2vec2-large-xls-r-300m - 0.077 8 GPU V100 23min here run.sh
Common Voice "sv-SE" facebook/wav2vec2-large-xls-r-300m - 0.099 8 GPU V100 23min here run.sh
Common Voice "tr" facebook/wav2vec2-large-xlsr-53 0.36 - 8 GPU V100 18min here run.sh
Common Voice "tr" facebook/wav2vec2-large-xlsr-53 0.31 - 8 GPU V100 1h05 here run.sh
Common Voice "tr" facebook/wav2vec2-large-xlsr-53 0.35 - 1 GPU V100 1h20min here run.sh
Common Voice "tr" facebook/wav2vec2-xls-r-300m 0.31 - 8 GPU V100 1h05 here run.sh
Common Voice "tr" facebook/wav2vec2-xls-r-1b 0.21 - 2 GPU Titan 24 GB RAM 15h10 here run.sh
Common Voice "tr" in streaming mode facebook/wav2vec2-xls-r-300m 0.29 - 4 GPU V100 3h31 here run.sh

Multilingual Librispeech CTC

Dataset Dataset Config Pretrained Model Word error rate on eval Phoneme error rate on eval GPU setup Training time Fine-tuned Model & Logs Command to reproduce
Multilingual Librispeech "german" facebook/wav2vec2-large-xlsr-53 0.13 - 1 GPU Titan 24 GB RAM 15h04 here run.sh
Multilingual Librispeech "german" facebook/wav2vec2-xls-r-300m 0.15 - 1 GPU Titan 24 GB RAM 15h04 here run.sh

Sequence to Sequence

The script run_speech_recognition_seq2seq.py can be used to fine-tune any Speech Sequence-to-Sequence Model for automatic speech recognition on one of the official speech recognition datasets or a custom dataset.

A very common use case is to leverage a pretrained speech encoding model, e.g. Wav2Vec2, HuBERT, XLSR-Wav2Vec2 with a pretrained text decoding model, e.g. Bart to create a SpeechEnocderDecoderModel. Consequently, the warm-started Speech-Encoder-Decoder model can be fine-tuned in this script.

As an example, let's instantiate a Wav2Vec2-2-Bart model with the SpeechEnocderDecoderModel framework:

First create an empty repo on hf.co:

huggingface-cli repo create wav2vec2-2-bart-base
git clone https://huggingface.co/<your-user-name>/wav2vec2-2-bart-base
cd wav2vec2-2-bart-base

Next, run the following script inside the just cloned repo:

from transformers import SpeechEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer, Wav2Vec2Processor

# checkpoints to leverage
encoder_id = "facebook/wav2vec2-base"
decoder_id = "facebook/bart-base"

# load and save speech-encoder-decoder model
# set some hyper-parameters for training and evaluation
model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id, encoder_add_adapter=True, encoder_feat_proj_dropout=0.0, encoder_layerdrop=0.0, max_length=200, num_beams=5)
model.config.decoder_start_token_id = model.decoder.config.bos_token_id
model.config.pad_token_id = model.decoder.config.pad_token_id
model.config.eos_token_id = model.decoder.config.eos_token_id
model.save_pretrained("./")

# load and save processor
feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)
tokenizer = AutoTokenizer.from_pretrained(decoder_id)
processor = Wav2Vec2Processor(feature_extractor, tokenizer)
processor.save_pretrained("./")

Finally, we can upload all files:

git lfs install
git add . && git commit -m "upload model files" && git push

and link the official run_speech_recognition_seq2seq.py script to the folder:

ln -s $(realpath <path/to/transformers>/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) ./

Note that we have added a randomly initialized adapter to wav2vec2-base with encoder_add_adapter=True which further samples the output sequence of wav2vec2-base along the time dimension. The reason is that by default a single output vector of wav2vec2-base has a receptive field of ca. 25ms (cf. with section 4.2 of the official Wav2Vec2 paper), which represents a little less a single character. BART on the other hand makes use of a sentence-piece tokenizer as an input processor so that a single hidden vector of bart-base represents ca. 4 characters. To better align the output of Wav2Vec2 and BART's hidden vectors for the cross-attention mechanism, we further subsample Wav2Vec2's output by a factor of 8 by adding a convolution-based adapter.

Having warm-started the speech-encoder-decoder model <your-user-name>/wav2vec2-2-bart, we can now fine-tune it on speech recognition.

In the script [run_speech_recognition_seq2seq], we load the warm-started model, the feature extractor, and the tokenizer, process a speech recognition dataset, and then make use of the Seq2SeqTrainer. Note that it is important to also align the decoder's vocabulary with the speech transcriptions of the dataset. E.g. the Librispeech has only captilized letters in the transcriptions, whereas BART was pretrained mostly on normalized text. Thus it is recommended to add --do_lower_case to the fine-tuning script when using a warm-started SpeechEncoderDecoderModel. The model is fine-tuned on the standard cross-entropy language modeling loss for sequence-to-sequence (just like T5 or BART in natural language processing).

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)

Single GPU Seq2Seq

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

python run_speech_recognition_seq2seq.py \
 	--nproc_per_node 8 run_speech_recognition_seq2seq.py \
	--dataset_name="librispeech_asr" \
	--model_name_or_path="./" \
	--dataset_config_name="clean" \
	--train_split_name="train.100" \
	--eval_split_name="validation" \
	--output_dir="./" \
	--preprocessing_num_workers="16" \
	--length_column_name="input_length" \
	--overwrite_output_dir \
	--num_train_epochs="5" \
	--per_device_train_batch_size="8" \
	--per_device_eval_batch_size="8" \
	--gradient_accumulation_steps="8" \
	--learning_rate="3e-4" \
	--warmup_steps="400" \
	--evaluation_strategy="steps" \
	--text_column_name="text" \
	--save_steps="400" \
	--eval_steps="400" \
	--logging_steps="10" \
	--save_total_limit="1" \
	--freeze_feature_encoder \
	--gradient_checkpointing \
	--fp16 \
	--group_by_length \
	--predict_with_generate \
	--generation_max_length="40" \
	--generation_num_beams="1" \
	--do_train --do_eval \
	--do_lower_case

On a single V100 GPU, this script should run in ca. 5 hours and yield a cross-entropy loss of 0.405 and word error rate of 0.0728.

Multi GPU Seq2Seq

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_seq2seq.py \
	--dataset_name="librispeech_asr" \
	--model_name_or_path="./" \
	--dataset_config_name="clean" \
	--train_split_name="train.100" \
	--eval_split_name="validation" \
	--output_dir="./" \
	--preprocessing_num_workers="16" \
	--length_column_name="input_length" \
	--overwrite_output_dir \
	--num_train_epochs="5" \
	--per_device_train_batch_size="8" \
	--per_device_eval_batch_size="8" \
	--gradient_accumulation_steps="1" \
	--learning_rate="3e-4" \
	--warmup_steps="400" \
	--evaluation_strategy="steps" \
	--text_column_name="text" \
	--save_steps="400" \
	--eval_steps="400" \
	--logging_steps="10" \
	--save_total_limit="1" \
	--freeze_feature_encoder \
	--gradient_checkpointing \
	--fp16 \
	--group_by_length \
	--predict_with_generate \
	--do_train --do_eval \
	--do_lower_case

On 8 V100 GPUs, this script should run in ca. 45 minutes and yield a cross-entropy loss of 0.405 and word error rate of 0.0728

Examples Seq2Seq

Librispeech Seq2Seq

Dataset Dataset Config Pretrained Model Word error rate on eval Phoneme error rate on eval GPU setup Training time Fine-tuned Model & Logs Command to reproduce
Librispeech "clean" - "train.100" facebook/wav2vec2-base and facebook/bart-base 0.0728 - 8 GPU V100 45min here create_model.py & run.sh
Librispeech "clean" - "train.100" facebook/wav2vec2-large-lv60 and facebook/bart-large 0.0486 - 8 GPU V100 1h20min here create_model.py & run.sh