Minor fixes to XTREME-S (#16193)

* Minor fixes

* Fix vocab union

* Update examples/research_projects/xtreme-s/README.md
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

* Update README

* unused import
Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>

examples/research_projects/xtreme-s/README.md

@@ -20,7 +20,7 @@ limitations under the License.
 
 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.
+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, zho_simpl, zho_trad, ces, 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 and zul.
 
 Paper: `<TODO>`
 
@@ -32,16 +32,14 @@ Based on the [`run_xtreme_s.py`](https://github.com/huggingface/transformers/blo
 
 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:
+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" \
-    --dataset_name="google/xtreme_s" \
-    --dataset_config_name="${TASK_NAME}" \
-    --eval_split_name="validation" \
+    --task="${TASK_NAME}" \
     --output_dir="xtreme_s_xlsr_${TASK_NAME}" \
     --num_train_epochs=100 \
     --per_device_train_batch_size=32 \
@@ -49,16 +47,16 @@ python run_xtreme_s.py \
     --target_column_name="transcription" \
     --save_steps=500 \
     --eval_steps=500 \
-    --freeze_feature_encoder \
     --gradient_checkpointing \
     --fp16 \
     --group_by_length \
     --do_train \
     --do_eval \
+    --do_predict \
     --push_to_hub
 ```
 
-where `TASK_NAME` can be one of: `mls.all, voxpopuli, covost2.all, fleurs.all, minds14.all`.
+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:
@@ -109,6 +107,7 @@ python -m torch.distributed.launch \
     --group_by_length \
     --do_train \
     --do_eval \
+    --do_predict \
     --metric_for_best_model="wer" \
     --greater_is_better=False \
     --load_best_model_at_end \
@@ -152,6 +151,7 @@ python -m torch.distributed.launch \
     --group_by_length \
     --do_train \
     --do_eval \
+    --do_predict \
     --metric_for_best_model="f1" \
     --greater_is_better=True \
     --load_best_model_at_end \

examples/research_projects/xtreme-s/run_xtreme_s.py

@@ -15,7 +15,6 @@
 
 """ Fine-tuning a 🤗 Transformers pretrained speech model on the XTREME-S benchmark tasks"""
 
-import functools
 import json
 import logging
 import os
@@ -152,8 +151,8 @@ class DataTrainingArguments:
     """
 
     dataset_name: str = field(
-        default="xtreme_s",
-        metadata={"help": "The name of the dataset to use (via the datasets library). Defaults to 'xtreme_s'"},
+        default="google/xtreme_s",
+        metadata={"help": "The name of the dataset to use (via the datasets library). Defaults to 'google/xtreme_s'"},
     )
     task: str = field(
         default=None,
@@ -169,21 +168,20 @@ class DataTrainingArguments:
     train_split_name: str = field(
         default="train",
         metadata={
-            "help": "The name of the training data set split to use (via the datasets library). " "Defaults to 'train'"
+            "help": "The name of the training dataset split to use (via the datasets library). Defaults to 'train'"
         },
     )
     eval_split_name: str = field(
         default="validation",
         metadata={
-            "help": "The name of the evaluation data set split to use (via the datasets library). "
+            "help": "The name of the evaluation dataset split to use (via the datasets library). "
             "Defaults to 'validation'"
         },
     )
     predict_split_name: str = field(
         default="test",
         metadata={
-            "help": "The name of the prediction data set split to use (via the datasets library). "
-            "Defaults to 'test'"
+            "help": "The name of the prediction dataset split to use (via the datasets library). " "Defaults to 'test'"
         },
     )
     audio_column_name: str = field(
@@ -191,10 +189,10 @@ class DataTrainingArguments:
         metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
     )
     target_column_name: str = field(
-        default="transcription",
+        default=None,
         metadata={
             "help": "The name of the dataset column containing the target data "
-            "(transcription/translation/label). Defaults to 'transcription'"
+            "(transcription/translation/label). If None, the name will be inferred from the task. Defaults to None."
         },
     )
     overwrite_cache: bool = field(
@@ -348,8 +346,10 @@ def create_vocabulary_from_data(
     )
 
     # take union of all unique characters in each dataset
-    vocab_set = functools.reduce(
-        lambda vocab_1, vocab_2: set(vocab_1["vocab"][0]) | set(vocab_2["vocab"][0]), vocabs.values()
+    vocab_set = (
+        (set(vocabs["train"]["vocab"][0]) if "train" in vocabs else set())
+        | (set(vocabs["eval"]["vocab"][0]) if "eval" in vocabs else set())
+        | (set(vocabs["predict"]["vocab"][0]) if "predict" in vocabs else set())
     )
 
     vocab_dict = {v: k for k, v in enumerate(sorted(list(vocab_set)))}
@@ -434,7 +434,10 @@ def main():
             " for multi-lingual fine-tuning."
         )
 
+    if data_args.target_column_name is None:
         target_column_name = TASK_TO_TARGET_COLUMN_NAME[task_name]
+    else:
+        target_column_name = data_args.target_column_name
 
     # here we differentiate between tasks with text as the target and classification tasks
     is_text_target = target_column_name in ("transcription", "translation")
@@ -457,9 +460,9 @@ def main():
                 f"{', '.join(raw_datasets['train'].column_names)}."
             )
 
-        if data_args.target_column_name not in raw_datasets["train"].column_names:
+        if target_column_name not in raw_datasets["train"].column_names:
             raise ValueError(
-                f"--target_column_name {data_args.target_column_name} not found in dataset '{data_args.dataset_name}'. "
+                f"--target_column_name {target_column_name} not found in dataset '{data_args.dataset_name}'. "
                 "Make sure to set `--target_column_name` to the correct text column - one of "
                 f"{', '.join(raw_datasets['train'].column_names)}."
             )
@@ -468,7 +471,7 @@ def main():
             raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
 
         if not is_text_target:
-            label_list = raw_datasets["train"].features[data_args.target_column_name].names
+            label_list = raw_datasets["train"].features[target_column_name].names
             num_labels = len(label_list)
 
     if training_args.do_eval:
@@ -684,7 +687,7 @@ def main():
         if is_text_target:
             batch["labels"] = tokenizer(batch["target_text"], **additional_kwargs).input_ids
         else:
-            batch["labels"] = batch[data_args.target_column_name]
+            batch["labels"] = batch[target_column_name]
         return batch
 
     with training_args.main_process_first(desc="dataset map preprocessing"):
@@ -809,10 +812,10 @@ def main():
         trainer.save_metrics("train", metrics)
         trainer.save_state()
 
-    # Evaluation
+    # Evaluation on the test set
     results = {}
     if training_args.do_predict:
-        logger.info("*** Predicte ***")
+        logger.info(f"*** Evaluating on the `{data_args.predict_split_name}` set ***")
         metrics = trainer.evaluate(vectorized_datasets["predict"])
         max_predict_samples = (
             data_args.max_predict_samples
@@ -831,9 +834,8 @@ def main():
         "tags": [task_name, data_args.dataset_name],
         "dataset_args": f"Config: {config_name}, Training split: {data_args.train_split_name}, Eval split: {data_args.eval_split_name}, Predict split: {data_args.predict_split_name}",
         "dataset": f"{data_args.dataset_name.upper()} - {config_name.upper()}",
+        "language": data_args.language,
     }
-    if "common_voice" in data_args.dataset_name:
-        kwargs["language"] = config_name
 
     if training_args.push_to_hub:
         trainer.push_to_hub(**kwargs)