161 lines
7.6 KiB
Markdown
161 lines
7.6 KiB
Markdown
<!---
|
|
Copyright 2022 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.
|
|
-->
|
|
|
|
# XTREME-S benchmark examples
|
|
|
|
*Maintainers: [Anton Lozhkov](https://github.com/anton-l) and [Patrick von Platen](https://github.com/patrickvonplaten)*
|
|
|
|
The Cross-lingual TRansfer Evaluation of Multilingual Encoders for Speech (XTREME-S) benchmark is a benchmark designed to evaluate speech representations across languages, tasks, domains and data regimes. It covers XX typologically diverse languages and seven downstream tasks grouped in four families: speech recognition, translation, classification and retrieval.
|
|
|
|
XTREME-S covers speech recognition with Fleurs, Multilingual LibriSpeech (MLS) and VoxPopuli, speech translation with CoVoST-2, speech classification with LangID (Fleurs) and intent classification (MInds-14) and finally speech(-text) retrieval with Fleurs. Each of the tasks covers a subset of the 102 languages included in XTREME-S (shown here with their ISO 3166-1 codes): afr, amh, ara, asm, ast, azj, bel, ben, bos, cat, ceb, ces, cmn, cym, dan, deu, ell, eng, spa, est, fas, ful, fin, tgl, fra, gle, glg, guj, hau, heb, hin, hrv, hun, hye, ind, ibo, isl, ita, jpn, jav, kat, kam, kea, kaz, khm, kan, kor, ckb, kir, ltz, lug, lin, lao, lit, luo, lav, mri, mkd, mal, mon, mar, msa, mlt, mya, nob, npi, nld, nso, nya, oci, orm, ory, pan, pol, pus, por, ron, rus, bul, snd, slk, slv, sna, som, srp, swe, swh, tam, tel, tgk, tha, tur, ukr, umb, urd, uzb, vie, wol, xho, yor, yue and zul.
|
|
|
|
Paper: [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752)
|
|
|
|
Dataset: [https://huggingface.co/datasets/google/xtreme_s](https://huggingface.co/datasets/google/xtreme_s)
|
|
|
|
## Fine-tuning for the XTREME-S tasks
|
|
|
|
Based on the [`run_xtreme_s.py`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/xtreme-s/run_xtreme_s.py) script.
|
|
|
|
This script can fine-tune any of the pretrained speech models on the [hub](https://huggingface.co/models?pipeline_tag=automatic-speech-recognition) on the [XTREME-S dataset](https://huggingface.co/datasets/google/xtreme_s) tasks.
|
|
|
|
XTREME-S is made up of 7 different tasks. Here is how to run the script on each of them:
|
|
|
|
```bash
|
|
export TASK_NAME=mls.all
|
|
|
|
python run_xtreme_s.py \
|
|
--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
|
|
--task="${TASK_NAME}" \
|
|
--output_dir="xtreme_s_xlsr_${TASK_NAME}" \
|
|
--num_train_epochs=100 \
|
|
--per_device_train_batch_size=32 \
|
|
--learning_rate="3e-4" \
|
|
--target_column_name="transcription" \
|
|
--save_steps=500 \
|
|
--eval_steps=500 \
|
|
--gradient_checkpointing \
|
|
--fp16 \
|
|
--group_by_length \
|
|
--do_train \
|
|
--do_eval \
|
|
--do_predict \
|
|
--push_to_hub
|
|
```
|
|
|
|
where `TASK_NAME` can be one of: `mls, voxpopuli, covost2, fleurs-asr, fleurs-lang_id, minds14`.
|
|
|
|
We get the following results on the test set of the benchmark's datasets.
|
|
The corresponding training commands for each dataset are given in the sections below:
|
|
|
|
| Task | Dataset | Result | Fine-tuned model & logs | Training time | GPUs |
|
|
|-----------------------|-----------|-----------------------|--------------------------------------------------------------------|---------------|--------|
|
|
| Speech Recognition | MLS | 30.33 WER | [here](https://huggingface.co/anton-l/xtreme_s_xlsr_300m_mls/) | 18:47:25 | 8xV100 |
|
|
| Speech Recognition | VoxPopuli | - | - | - | - |
|
|
| Speech Recognition | FLEURS | - | - | - | - |
|
|
| Speech Translation | CoVoST-2 | - | - | - | - |
|
|
| Speech Classification | Minds-14 | 90.15 F1 / 90.33 Acc. | [here](https://huggingface.co/anton-l/xtreme_s_xlsr_300m_minds14/) | 2:54:21 | 2xA100 |
|
|
| Speech Classification | FLEURS | - | - | - | - |
|
|
| Speech Retrieval | FLEURS | - | - | - | - |
|
|
|
|
### Speech Recognition with MLS
|
|
|
|
The following command shows how to fine-tune the [XLS-R](https://huggingface.co/docs/transformers/main/model_doc/xls_r) model on [XTREME-S MLS](https://huggingface.co/datasets/google/xtreme_s#multilingual-librispeech-mls) using 8 GPUs in half-precision.
|
|
|
|
```bash
|
|
python -m torch.distributed.launch \
|
|
--nproc_per_node=8 \
|
|
run_xtreme_s.py \
|
|
--task="mls" \
|
|
--language="all" \
|
|
--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
|
|
--output_dir="xtreme_s_xlsr_300m_mls" \
|
|
--overwrite_output_dir \
|
|
--num_train_epochs=100 \
|
|
--per_device_train_batch_size=4 \
|
|
--per_device_eval_batch_size=1 \
|
|
--gradient_accumulation_steps=2 \
|
|
--learning_rate="3e-4" \
|
|
--warmup_steps=3000 \
|
|
--eval_strategy="steps" \
|
|
--max_duration_in_seconds=20 \
|
|
--save_steps=500 \
|
|
--eval_steps=500 \
|
|
--logging_steps=1 \
|
|
--layerdrop=0.0 \
|
|
--mask_time_prob=0.3 \
|
|
--mask_time_length=10 \
|
|
--mask_feature_prob=0.1 \
|
|
--mask_feature_length=64 \
|
|
--freeze_feature_encoder \
|
|
--gradient_checkpointing \
|
|
--fp16 \
|
|
--group_by_length \
|
|
--do_train \
|
|
--do_eval \
|
|
--do_predict \
|
|
--metric_for_best_model="wer" \
|
|
--greater_is_better=False \
|
|
--load_best_model_at_end \
|
|
--push_to_hub
|
|
```
|
|
|
|
On 8 V100 GPUs, this script should run in ~19 hours and yield a cross-entropy loss of **0.6215** and word error rate of **30.33**
|
|
|
|
### Speech Classification with Minds-14
|
|
|
|
The following command shows how to fine-tune the [XLS-R](https://huggingface.co/docs/transformers/main/model_doc/xls_r) model on [XTREME-S MLS](https://huggingface.co/datasets/google/xtreme_s#intent-classification---minds-14) using 2 GPUs in half-precision.
|
|
|
|
```bash
|
|
python -m torch.distributed.launch \
|
|
--nproc_per_node=2 \
|
|
run_xtreme_s.py \
|
|
--task="minds14" \
|
|
--language="all" \
|
|
--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
|
|
--output_dir="xtreme_s_xlsr_300m_minds14" \
|
|
--overwrite_output_dir \
|
|
--num_train_epochs=50 \
|
|
--per_device_train_batch_size=32 \
|
|
--per_device_eval_batch_size=8 \
|
|
--gradient_accumulation_steps=1 \
|
|
--learning_rate="3e-4" \
|
|
--warmup_steps=1500 \
|
|
--eval_strategy="steps" \
|
|
--max_duration_in_seconds=30 \
|
|
--save_steps=200 \
|
|
--eval_steps=200 \
|
|
--logging_steps=1 \
|
|
--layerdrop=0.0 \
|
|
--mask_time_prob=0.3 \
|
|
--mask_time_length=10 \
|
|
--mask_feature_prob=0.1 \
|
|
--mask_feature_length=64 \
|
|
--freeze_feature_encoder \
|
|
--gradient_checkpointing \
|
|
--fp16 \
|
|
--group_by_length \
|
|
--do_train \
|
|
--do_eval \
|
|
--do_predict \
|
|
--metric_for_best_model="f1" \
|
|
--greater_is_better=True \
|
|
--load_best_model_at_end \
|
|
--push_to_hub
|
|
```
|
|
|
|
On 2 A100 GPUs, this script should run in ~5 hours and yield a cross-entropy loss of **0.4119** and F1 score of **90.15**
|