[XLSR-Wav2Vec2] Add multi-lingual Wav2Vec2 models

README.md

@@ -237,6 +237,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
 1. **[XLM-ProphetNet](https://huggingface.co/transformers/model_doc/xlmprophetnet.html)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
 1. **[XLNet](https://huggingface.co/transformers/model_doc/xlnet.html)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+1. **[XLSR-Wav2Vec2](https://huggingface.co/transformers/model_doc/xlsr_wav2vec2.html)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 1. Want to contribute a new model? We have added a **detailed guide and templates** to guide you in the process of adding a new model. You can find them in the [`templates`](./templates) folder of the repository. Be sure to check the [contributing guidelines](./CONTRIBUTING.md) and contact the maintainers or open an issue to collect feedbacks before starting your PR.
 
 To check if each model has an implementation in PyTorch/TensorFlow/Flax or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/transformers/index.html#bigtable)

docs/source/index.rst

@@ -221,6 +221,9 @@ and conversion utilities for the following models:
 46. :doc:`XLNet <model_doc/xlnet>` (from Google/CMU) released with the paper `​XLNet: Generalized Autoregressive
     Pretraining for Language Understanding <https://arxiv.org/abs/1906.08237>`__ by Zhilin Yang*, Zihang Dai*, Yiming
     Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
+47. :doc:`XLSR-Wav2Vec2 <model_doc/xlsr_wav2vec2>` (from Facebook AI) released with the paper `Unsupervised
+    Cross-Lingual Representation Learning For Speech Recognition <https://arxiv.org/abs/2006.13979>`__ by Alexis
+    Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 
 
 .. _bigtable:
@@ -451,6 +454,7 @@ TensorFlow and/or Flax.
     model_doc/xlmprophetnet
     model_doc/xlmroberta
     model_doc/xlnet
+    model_doc/xlsr_wav2vec2
 
 .. toctree::
     :maxdepth: 2

docs/source/model_doc/xlsr_wav2vec2.rst

@@ -0,0 +1,45 @@
+.. 
+    Copyright 2021 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.
+
+XLSR-Wav2Vec2
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The XLSR-Wav2Vec2 model was proposed in `Unsupervised Cross-Lingual Representation Learning For Speech Recognition
+<https://arxiv.org/abs/2006.13979>`__ by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael
+Auli.
+
+The abstract from the paper is the following:
+
+*This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw
+waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over
+masked latent speech representations and jointly learns a quantization of the latents shared across languages. The
+resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly
+outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction
+of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to
+a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong
+individual models. Analysis shows that the latent discrete speech representations are shared across languages with
+increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing
+XLSR-53, a large model pretrained in 53 languages.*
+
+Tips:
+
+- XLSR-Wav2Vec2 is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
+- XLSR-Wav2Vec2 model was trained using connectionist temporal classification (CTC) so the model output has to be
+  decoded using :class:`~transformers.Wav2Vec2CTCTokenizer`.
+
+XLSR-Wav2Vec2's architecture is based on the Wav2Vec2 model, so one can refer to :doc:`Wav2Vec2's documentation page
+<wav2vec2>`.
+
+The original code can be found `here <https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec>`__.

src/transformers/models/wav2vec2/convert_wav2vec2_original_pytorch_checkpoint_to_pytorch.py

@@ -90,7 +90,8 @@ def recursively_load_weights(fairseq_model, hf_model, is_finetuned):
         else:
             for key, mapped_key in MAPPING.items():
                 mapped_key = "wav2vec2." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key
-                if key in name:
+
+                if key in name or (key.split("w2v_model.")[-1] == name.split(".")[0] and not is_finetuned):
                     is_used = True
                     if "*" in mapped_key:
                         layer_index = name.split(key)[0].split(".")[-2]
@@ -110,7 +111,7 @@ def recursively_load_weights(fairseq_model, hf_model, is_finetuned):
         if not is_used:
             unused_weights.append(name)
 
-    logger.info("Unused weights", unused_weights)
+    logger.warn(f"Unused weights: {unused_weights}")
 
 
 def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):