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S2T Example: Speech Translation (ST) on MuST-C
MuST-C is multilingual speech-to-text translation corpus with 8-language translations on English TED talks. We match the state-of-the-art performance in ESPNet-ST with a simpler model training pipeline.
Data Preparation
Download and unpack MuST-C data to a path
${MUSTC_ROOT}/en-${TARGET_LANG_ID}, then preprocess it with
# additional Python packages for S2T data processing/model training
pip install pandas torchaudio soundfile sentencepiece
# Generate TSV manifests, features, vocabulary
# and configuration for each language
python examples/speech_to_text/prep_mustc_data.py \
--data-root ${MUSTC_ROOT} --task asr \
--vocab-type unigram --vocab-size 5000
python examples/speech_to_text/prep_mustc_data.py \
--data-root ${MUSTC_ROOT} --task st \
--vocab-type unigram --vocab-size 8000
# Add vocabulary and configuration for joint data
# (based on the manifests and features generated above)
python examples/speech_to_text/prep_mustc_data.py \
--data-root ${MUSTC_ROOT} --task asr --joint \
--vocab-type unigram --vocab-size 10000
python examples/speech_to_text/prep_mustc_data.py \
--data-root ${MUSTC_ROOT} --task st --joint \
--vocab-type unigram --vocab-size 10000
The generated files (manifest, features, vocabulary and data configuration) will be added to
${MUSTC_ROOT}/en-${TARGET_LANG_ID} (per-language data) and MUSTC_ROOT (joint data).
Download our vocabulary files if you want to use our pre-trained models:
- ASR: En-De, En-Nl, En-Es, En-Fr, En-It, En-Pt, En-Ro, En-Ru, Joint
- ST: En-De, En-Nl, En-Es, En-Fr, En-It, En-Pt, En-Ro, En-Ru, Multilingual
ASR
Training
En-De as example:
fairseq-train ${MUSTC_ROOT}/en-de \
--config-yaml config_asr.yaml --train-subset train_asr --valid-subset dev_asr \
--save-dir ${ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \
--task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \
--arch s2t_transformer_s --optimizer adam --lr 1e-3 --lr-scheduler inverse_sqrt \
--warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8
For joint model (using ASR data from all 8 directions):
fairseq-train ${MUSTC_ROOT} \
--config-yaml config_asr.yaml \
--train-subset train_de_asr,train_nl_asr,train_es_asr,train_fr_asr,train_it_asr,train_pt_asr,train_ro_asr,train_ru_asr \
--valid-subset dev_de_asr,dev_nl_asr,dev_es_asr,dev_fr_asr,dev_it_asr,dev_pt_asr,dev_ro_asr,dev_ru_asr \
--save-dir ${JOINT_ASR_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \
--task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \
--arch s2t_transformer_s --optimizer adam --lr 1e-3 --lr-scheduler inverse_sqrt \
--warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8
where ASR_SAVE_DIR (JOINT_ASR_SAVE_DIR) is the checkpoint root path. We set --update-freq 8 to simulate 8 GPUs
with 1 GPU. You may want to update it accordingly when using more than 1 GPU.
Inference & Evaluation
CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt
python scripts/average_checkpoints.py \
--inputs ${ASR_SAVE_DIR} --num-epoch-checkpoints 10 \
--output "${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}"
fairseq-generate ${MUSTC_ROOT}/en-de \
--config-yaml config_asr.yaml --gen-subset tst-COMMON_asr --task speech_to_text \
--path ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \
--scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct
# For models trained on joint data
python scripts/average_checkpoints.py \
--inputs ${JOINT_ASR_SAVE_DIR} --num-epoch-checkpoints 10 \
--output "${JOINT_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}"
for LANG in de nl es fr it pt ro ru; do
fairseq-generate ${MUSTC_ROOT} \
--config-yaml config_asr.yaml --gen-subset tst-COMMON_${LANG}_asr --task speech_to_text \
--path ${JOINT_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME} --max-tokens 50000 --beam 5 \
--scoring wer --wer-tokenizer 13a --wer-lowercase --wer-remove-punct
done
Results
| Data | --arch | Params | En-De | En-Nl | En-Es | En-Fr | En-It | En-Pt | En-Ro | En-Ru | Model |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Single | s2t_transformer_s | 31M | 18.2 | 17.6 | 17.7 | 17.2 | 17.9 | 19.1 | 18.1 | 17.7 | (<-Download) |
| Joint | s2t_transformer_m | 76M | 16.8 | 16.7 | 16.9 | 16.9 | 17.0 | 17.4 | 17.0 | 16.9 | Download |
ST
Training
En-De as example:
fairseq-train ${MUSTC_ROOT}/en-de \
--config-yaml config_st.yaml --train-subset train_st --valid-subset dev_st \
--save-dir ${ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \
--task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \
--arch s2t_transformer_s --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \
--warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \
--load-pretrained-encoder-from ${ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}
For multilingual model (all 8 directions):
fairseq-train ${MUSTC_ROOT} \
--config-yaml config_st.yaml \
--train-subset train_de_st,train_nl_st,train_es_st,train_fr_st,train_it_st,train_pt_st,train_ro_st,train_ru_st \
--valid-subset dev_de_st,dev_nl_st,dev_es_st,dev_fr_st,dev_it_st,dev_pt_st,dev_ro_st,dev_ru_st \
--save-dir ${MULTILINGUAL_ST_SAVE_DIR} --num-workers 4 --max-tokens 40000 --max-update 100000 \
--task speech_to_text --criterion label_smoothed_cross_entropy --label-smoothing 0.1 --report-accuracy \
--arch s2t_transformer_s --ignore-prefix-size 1 --optimizer adam --lr 2e-3 --lr-scheduler inverse_sqrt \
--warmup-updates 10000 --clip-norm 10.0 --seed 1 --update-freq 8 \
--load-pretrained-encoder-from ${JOINT_ASR_SAVE_DIR}/${CHECKPOINT_FILENAME}
where ST_SAVE_DIR (MULTILINGUAL_ST_SAVE_DIR) is the checkpoint root path. The ST encoder is pre-trained by ASR
for faster training and better performance: --load-pretrained-encoder-from <(JOINT_)ASR checkpoint path>. We set
--update-freq 8 to simulate 8 GPUs with 1 GPU. You may want to update it accordingly when using more than 1 GPU.
For multilingual models, we prepend target language ID token as target BOS, which should be excluded from
the training loss via --ignore-prefix-size 1.
Inference & Evaluation
Average the last 10 checkpoints and evaluate on the tst-COMMON split:
CHECKPOINT_FILENAME=avg_last_10_checkpoint.pt
python scripts/average_checkpoints.py \
--inputs ${ST_SAVE_DIR} --num-epoch-checkpoints 10 \
--output "${ST_SAVE_DIR}/${CHECKPOINT_FILENAME}"
fairseq-generate ${MUSTC_ROOT}/en-de \
--config-yaml config_st.yaml --gen-subset tst-COMMON_st --task speech_to_text \
--path ${ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \
--max-tokens 50000 --beam 5 --scoring sacrebleu
# For multilingual models
python scripts/average_checkpoints.py \
--inputs ${MULTILINGUAL_ST_SAVE_DIR} --num-epoch-checkpoints 10 \
--output "${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME}"
for LANG in de nl es fr it pt ro ru; do
fairseq-generate ${MUSTC_ROOT} \
--config-yaml config_st.yaml --gen-subset tst-COMMON_${LANG}_st --task speech_to_text \
--prefix-size 1 --path ${MULTILINGUAL_ST_SAVE_DIR}/${CHECKPOINT_FILENAME} \
--max-tokens 50000 --beam 5 --scoring sacrebleu
done
For multilingual models, we force decoding from the target language ID token (as BOS) via --prefix-size 1.