Improve mismatched sizes management when loading a pretrained model (#17257)

- Add --ignore_mismatched_sizes argument to classification examples

- Expand the error message when loading a model whose head dimensions are different from expected dimensions

examples/pytorch/audio-classification/README.md

@@ -18,13 +18,13 @@ limitations under the License.
 
 The following examples showcase how to fine-tune `Wav2Vec2` for audio classification using PyTorch.
 
-Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, 
-*e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html), 
-[HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html), 
-[XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), have shown to require only 
+Speech recognition models that have been pretrained in unsupervised fashion on audio data alone,
+*e.g.* [Wav2Vec2](https://huggingface.co/transformers/main/model_doc/wav2vec2.html),
+[HuBERT](https://huggingface.co/transformers/main/model_doc/hubert.html),
+[XLSR-Wav2Vec2](https://huggingface.co/transformers/main/model_doc/xlsr_wav2vec2.html), have shown to require only
 very little annotated data to yield good performance on speech classification datasets.
 
-## Single-GPU 
+## Single-GPU
 
 The following command shows how to fine-tune [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the 🗣️ [Keyword Spotting subset](https://huggingface.co/datasets/superb#ks) of the SUPERB dataset.
 
@@ -63,7 +63,9 @@ On a single V100 GPU (16GB), this script should run in ~14 minutes and yield acc
 
 👀 See the results here: [anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting)
 
-## Multi-GPU 
+> If your model classification head dimensions do not fit the number of labels in the dataset, you can specify `--ignore_mismatched_sizes` to adapt it.
+
+## Multi-GPU
 
 The following command shows how to fine-tune [wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) for 🌎 **Language Identification** on the [CommonLanguage dataset](https://huggingface.co/datasets/anton-l/common_language).
 
@@ -139,7 +141,7 @@ It has been verified that the script works for the following datasets:
 
 | Dataset | Pretrained Model | # transformer layers | Accuracy on eval | GPU setup | Training time | Fine-tuned Model & Logs |
 |---------|------------------|----------------------|------------------|-----------|---------------|--------------------------|
-| Keyword Spotting | [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) | 2 | 0.9706 | 1 V100 GPU | 11min  | [here](https://huggingface.co/anton-l/distilhubert-ft-keyword-spotting) | 
+| Keyword Spotting | [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) | 2 | 0.9706 | 1 V100 GPU | 11min  | [here](https://huggingface.co/anton-l/distilhubert-ft-keyword-spotting) |
 | Keyword Spotting | [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 12 | 0.9826 | 1 V100 GPU | 14min  | [here](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting) |
 | Keyword Spotting | [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) | 12 | 0.9819 | 1 V100 GPU | 14min  | [here](https://huggingface.co/anton-l/hubert-base-ft-keyword-spotting) |
 | Keyword Spotting | [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 24 | 0.9757 | 1 V100 GPU | 15min  | [here](https://huggingface.co/anton-l/sew-mid-100k-ft-keyword-spotting) |

examples/pytorch/audio-classification/run_audio_classification.py

@@ -163,6 +163,10 @@ class ModelArguments:
     freeze_feature_extractor: Optional[bool] = field(
         default=None, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
     )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
 
     def __post_init__(self):
         if not self.freeze_feature_extractor and self.freeze_feature_encoder:
@@ -333,6 +337,7 @@ def main():
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
     )
 
     # freeze the convolutional waveform encoder