161 lines
7.6 KiB
Markdown
161 lines
7.6 KiB
Markdown
<!---
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Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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-->
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# XTREME-S benchmark examples
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*Maintainers: [Anton Lozhkov](https://github.com/anton-l) and [Patrick von Platen](https://github.com/patrickvonplaten)*
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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.
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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.
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Paper: [XTREME-S: Evaluating Cross-lingual Speech Representations](https://arxiv.org/abs/2203.10752)
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Dataset: [https://huggingface.co/datasets/google/xtreme_s](https://huggingface.co/datasets/google/xtreme_s)
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## Fine-tuning for the XTREME-S tasks
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Based on the [`run_xtreme_s.py`](https://github.com/huggingface/transformers/blob/main/examples/research_projects/xtreme-s/run_xtreme_s.py) script.
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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.
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XTREME-S is made up of 7 different tasks. Here is how to run the script on each of them:
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```bash
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export TASK_NAME=mls.all
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python run_xtreme_s.py \
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--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
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--task="${TASK_NAME}" \
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--output_dir="xtreme_s_xlsr_${TASK_NAME}" \
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--num_train_epochs=100 \
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--per_device_train_batch_size=32 \
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--learning_rate="3e-4" \
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--target_column_name="transcription" \
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--save_steps=500 \
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--eval_steps=500 \
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--gradient_checkpointing \
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--fp16 \
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--group_by_length \
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--do_train \
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--do_eval \
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--do_predict \
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--push_to_hub
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```
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where `TASK_NAME` can be one of: `mls, voxpopuli, covost2, fleurs-asr, fleurs-lang_id, minds14`.
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We get the following results on the test set of the benchmark's datasets.
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The corresponding training commands for each dataset are given in the sections below:
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| Task | Dataset | Result | Fine-tuned model & logs | Training time | GPUs |
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|-----------------------|-----------|-----------------------|--------------------------------------------------------------------|---------------|--------|
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| Speech Recognition | MLS | 30.33 WER | [here](https://huggingface.co/anton-l/xtreme_s_xlsr_300m_mls/) | 18:47:25 | 8xV100 |
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| Speech Recognition | VoxPopuli | - | - | - | - |
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| Speech Recognition | FLEURS | - | - | - | - |
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| Speech Translation | CoVoST-2 | - | - | - | - |
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| 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 |
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| Speech Classification | FLEURS | - | - | - | - |
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| Speech Retrieval | FLEURS | - | - | - | - |
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### Speech Recognition with MLS
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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.
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```bash
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python -m torch.distributed.launch \
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--nproc_per_node=8 \
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run_xtreme_s.py \
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--task="mls" \
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--language="all" \
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--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
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--output_dir="xtreme_s_xlsr_300m_mls" \
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--overwrite_output_dir \
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--num_train_epochs=100 \
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--per_device_train_batch_size=4 \
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--per_device_eval_batch_size=1 \
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--gradient_accumulation_steps=2 \
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--learning_rate="3e-4" \
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--warmup_steps=3000 \
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--evaluation_strategy="steps" \
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--max_duration_in_seconds=20 \
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--save_steps=500 \
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--eval_steps=500 \
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--logging_steps=1 \
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--layerdrop=0.0 \
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--mask_time_prob=0.3 \
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--mask_time_length=10 \
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--mask_feature_prob=0.1 \
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--mask_feature_length=64 \
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--freeze_feature_encoder \
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--gradient_checkpointing \
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--fp16 \
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--group_by_length \
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--do_train \
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--do_eval \
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--do_predict \
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--metric_for_best_model="wer" \
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--greater_is_better=False \
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--load_best_model_at_end \
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--push_to_hub
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```
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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**
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### Speech Classification with Minds-14
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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.
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```bash
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python -m torch.distributed.launch \
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--nproc_per_node=2 \
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run_xtreme_s.py \
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--task="minds14" \
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--language="all" \
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--model_name_or_path="facebook/wav2vec2-xls-r-300m" \
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--output_dir="xtreme_s_xlsr_300m_minds14" \
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--overwrite_output_dir \
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--num_train_epochs=50 \
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--per_device_train_batch_size=32 \
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--per_device_eval_batch_size=8 \
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--gradient_accumulation_steps=1 \
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--learning_rate="3e-4" \
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--warmup_steps=1500 \
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--evaluation_strategy="steps" \
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--max_duration_in_seconds=30 \
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--save_steps=200 \
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--eval_steps=200 \
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--logging_steps=1 \
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--layerdrop=0.0 \
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--mask_time_prob=0.3 \
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--mask_time_length=10 \
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--mask_feature_prob=0.1 \
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--mask_feature_length=64 \
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--freeze_feature_encoder \
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--gradient_checkpointing \
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--fp16 \
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--group_by_length \
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--do_train \
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--do_eval \
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--do_predict \
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--metric_for_best_model="f1" \
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--greater_is_better=True \
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--load_best_model_at_end \
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--push_to_hub
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```
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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**
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