Add the XTREME-S fine-tuning example (#15985)

* CTC+classification draft

* CTC+classification draft

* style

* multilingual runs

* Fix race condition during processor.from_reatrained

* Merge covost experiments

* Add README

* Quality

* Switch to .all configs

* Fix typos

examples/research_projects/xreme-s/README.md

+<!---
+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 BABEL, Multilingual LibriSpeech (MLS) and VoxPopuli, speech translation with CoVoST-2, speech classification with LangID (FLoRes) and intent classification (MInds-14) and finally speech retrieval with speech-speech translation data mining (bi-speech retrieval). Each of the tasks covers a subset of the 40 languages included in XTREME-S (shown here with their ISO 639-1 codes): ar, as, ca, cs, cy, da, de, en, en, en, en, es, et, fa, fi, fr, hr, hu, id, it, ja, ka, ko, lo, lt, lv, mn, nl, pl, pt, ro, ru, sk, sl, sv, sw, ta, tl, tr and zh.
+
+Paper: `<TODO>`
+
+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/master/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 task-specific subsets. 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" \
+    --dataset_name="google/xtreme_s" \
+    --dataset_config_name="${TASK_NAME}" \
+    --eval_split_name="validation" \
+    --output_dir="xtreme_s_xlsr_${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 \
+    --freeze_feature_encoder \
+    --gradient_checkpointing \
+    --fp16 \
+    --group_by_length \
+    --do_train \
+    --do_eval \
+    --push_to_hub
+```
+
+where `TASK_NAME` can be one of: `mls.all, voxpopuli, covost2.all, fleurs.all, minds14.all`.
+
+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  | 94.74 F1 / 94.70 Acc. | [here](https://huggingface.co/anton-l/xtreme_s_xlsr_300m_minds14/) | 04:46:40      | 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/master/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 \
+    --model_name_or_path="facebook/wav2vec2-xls-r-300m" \
+    --dataset_name="google/xtreme_s" \
+    --dataset_config_name="mls.all" \
+    --eval_split_name="test" \
+    --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 \
+    --evaluation_strategy="steps" \
+    --target_column_name="transcription" \
+    --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 \
+    --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/master/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 \
+    --model_name_or_path="facebook/wav2vec2-xls-r-300m" \
+    --dataset_name="google/xtreme_s" \
+    --dataset_config_name="minds14.all" \
+    --eval_split_name="test" \
+    --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 \
+    --evaluation_strategy="steps" \
+    --target_column_name="intent_class" \
+    --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 \
+    --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 **0.2890** and F1 score of **94.74**

examples/research_projects/xreme-s/requirements.txt

+datasets >= 1.18.0
+torch >= 1.5
+torchaudio
+librosa
+jiwer