Wav2Vec2 meets phonemes (#14353)

* up

* add tokenizer

* improve more

* finish tokenizer

* finish

* adapt speech recognition script

* adapt convert

* more fixes

* more fixes

* update phonemizer wav2vec2

* better naming

* fix more tests

* more fixes swedish

* correct tests

* finish

* improve script

* remove file

* up

* lets get those 100 model architectures until the end of the month

* make fix-copies

* correct more

* correct script

* more fixes

* more fixes

* add to docs

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* replace assert

* fix copies

* fix docs

* new try docs

* boom boom

* update

* add phonemizer to audio tests

* make fix-copies

* up

* upload models

* some changes

* Update tests/test_tokenization_wav2vec2_phoneme.py

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* more fixes

* remove @

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

docs/source/model_doc/wav2vec2_phoneme.mdx

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+<!--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.
+-->
+
+# Wav2Vec2Phoneme
+
+## Overview
+
+The Wav2Vec2Phoneme model was proposed in [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition (Xu et al.,
+2021](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
+
+The abstract from the paper is the following:
+
+*Recent progress in self-training, self-supervised pretraining and unsupervised learning enabled well performing speech
+recognition systems without any labeled data. However, in many cases there is labeled data available for related
+languages which is not utilized by these methods. This paper extends previous work on zero-shot cross-lingual transfer
+learning by fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. This is done by
+mapping phonemes of the training languages to the target language using articulatory features. Experiments show that
+this simple method significantly outperforms prior work which introduced task-specific architectures and used only part
+of a monolingually pretrained model.*
+
+Tips:
+
+- Wav2Vec2Phoneme uses the exact same architecture as Wav2Vec2
+- Wav2Vec2Phoneme is a speech model that accepts a float array corresponding to the raw waveform of the speech signal.
+- Wav2Vec2Phoneme model was trained using connectionist temporal classification (CTC) so the model output has to be
+  decoded using [`Wav2Vec2PhonemeCTCTokenizer`].
+- Wav2Vec2Phoneme can be fine-tuned on multiple language at once and decode unseen languages in a single forward pass
+  to a sequence of phonemes
+- By default the model outputs a sequence of phonemes. In order to transform the phonemes to a sequence of words one
+  should make use of a dictionary and language model.
+
+Relevant checkpoints can be found under https://huggingface.co/models?other=phoneme-recognition.
+
+This model was contributed by [patrickvonplaten](https://huggingface.co/patrickvonplaten)
+
+The original code can be found [here](https://github.com/pytorch/fairseq/tree/master/fairseq/models/wav2vec).
+
+Wav2Vec2Phoneme's architecture is based on the Wav2Vec2 model, so one can refer to [`Wav2Vec2`]'s documentation page except for the tokenizer.
+
+
+## Wav2Vec2PhonemeCTCTokenizer
+
+[[autodoc]] Wav2Vec2PhonemeCTCTokenizer
+	- __call__
+	- batch_decode
+	- decode
+	- phonemize