This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
Add bark. This PR aims at integrating Bark, a TTS model, to `transformers`.
`Bark` was designed and trained by [Suno-AI team](https://github.com/suno-ai/bark) and is made of 4 main components:

- A `semantic model ` (also named `text model`), i.e a causal autoregressive transformer (GPT2-like), which takes into input a tokenized text
- A `coarse acoustics model` (also named `coarse model`), also a causal autoregressive transformer, taking into input the results of the last model. It aims at regressing the first two audio codebooks necessary to `encodec`.
- A `fine acoustics model` (`fine model`), this time a non-causal autoencoder transformer, which iteratively predicts the 6 last codebooks based on the sum of the previous codebooks embeddings.
- having predicted 8 codebooks channels of `encodec`, Bark uses `encodec` to generate the output audio array.

Note that each of the first 3 modules can take optional conditional speaker embeddings aiming at conditioning the output audio according to `specific preset voices.
<!---
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.
-->

# Audio classification examples

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
very little annotated data to yield good performance on speech classification datasets.

## 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.

```bash
python run_audio_classification.py \
    --model_name_or_path facebook/wav2vec2-base \
    --dataset_name superb \
    --dataset_config_name ks \
    --output_dir wav2vec2-base-ft-keyword-spotting \
    --overwrite_output_dir \
    --remove_unused_columns False \
    --do_train \
    --do_eval \
    --fp16 \
    --learning_rate 3e-5 \
    --max_length_seconds 1 \
    --attention_mask False \
    --warmup_ratio 0.1 \
    --num_train_epochs 5 \
    --per_device_train_batch_size 32 \
    --gradient_accumulation_steps 4 \
    --per_device_eval_batch_size 32 \
    --dataloader_num_workers 4 \
    --logging_strategy steps \
    --logging_steps 10 \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --metric_for_best_model accuracy \
    --save_total_limit 3 \
    --seed 0 \
    --push_to_hub
```

On a single V100 GPU (16GB), this script should run in ~14 minutes and yield accuracy of **98.26%**.

👀 See the results here: [anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting)

> 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).

```bash
python run_audio_classification.py \
    --model_name_or_path facebook/wav2vec2-base \
    --dataset_name common_language \
    --audio_column_name audio \
    --label_column_name language \
    --output_dir wav2vec2-base-lang-id \
    --overwrite_output_dir \
    --remove_unused_columns False \
    --do_train \
    --do_eval \
    --fp16 \
    --learning_rate 3e-4 \
    --max_length_seconds 16 \
    --attention_mask False \
    --warmup_ratio 0.1 \
    --num_train_epochs 10 \
    --per_device_train_batch_size 8 \
    --gradient_accumulation_steps 4 \
    --per_device_eval_batch_size 1 \
    --dataloader_num_workers 8 \
    --logging_strategy steps \
    --logging_steps 10 \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --metric_for_best_model accuracy \
    --save_total_limit 3 \
    --seed 0 \
    --push_to_hub
```

On 4 V100 GPUs (16GB), this script should run in ~1 hour and yield accuracy of **79.45%**.

👀 See the results here: [anton-l/wav2vec2-base-lang-id](https://huggingface.co/anton-l/wav2vec2-base-lang-id)

## Sharing your model on 🤗 Hub

0. If you haven't already, [sign up](https://huggingface.co/join) for a 🤗 account

1. Make sure you have `git-lfs` installed and git set up.

```bash
$ apt install git-lfs
```

2. Log in with your HuggingFace account credentials using `huggingface-cli`

```bash
$ huggingface-cli login
# ...follow the prompts
```

3. When running the script, pass the following arguments:

```bash
python run_audio_classification.py \
    --push_to_hub \
    --hub_model_id <username/model_id> \
    ...
```

### Examples

The following table shows a couple of demonstration fine-tuning runs.
It has been verified that the script works for the following datasets:

- [SUPERB Keyword Spotting](https://huggingface.co/datasets/superb#ks)
- [Common Language](https://huggingface.co/datasets/common_language)

| 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 | [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) |
| Common Language | [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 12 | 0.7945 | 4 V100 GPUs | 1h10m  | [here](https://huggingface.co/anton-l/wav2vec2-base-lang-id) |

<!---
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.
-->

# Audio classification examples

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
very little annotated data to yield good performance on speech classification datasets.

## 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.

```bash
python run_audio_classification.py \
    --model_name_or_path facebook/wav2vec2-base \
    --dataset_name superb \
    --dataset_config_name ks \
    --output_dir wav2vec2-base-ft-keyword-spotting \
    --overwrite_output_dir \
    --remove_unused_columns False \
    --do_train \
    --do_eval \
    --fp16 \
    --learning_rate 3e-5 \
    --max_length_seconds 1 \
    --attention_mask False \
    --warmup_ratio 0.1 \
    --num_train_epochs 5 \
    --per_device_train_batch_size 32 \
    --gradient_accumulation_steps 4 \
    --per_device_eval_batch_size 32 \
    --dataloader_num_workers 4 \
    --logging_strategy steps \
    --logging_steps 10 \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --metric_for_best_model accuracy \
    --save_total_limit 3 \
    --seed 0 \
    --push_to_hub
```

On a single V100 GPU (16GB), this script should run in ~14 minutes and yield accuracy of **98.26%**.

👀 See the results here: [anton-l/wav2vec2-base-ft-keyword-spotting](https://huggingface.co/anton-l/wav2vec2-base-ft-keyword-spotting)

> 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).

```bash
python run_audio_classification.py \
    --model_name_or_path facebook/wav2vec2-base \
    --dataset_name common_language \
    --audio_column_name audio \
    --label_column_name language \
    --output_dir wav2vec2-base-lang-id \
    --overwrite_output_dir \
    --remove_unused_columns False \
    --do_train \
    --do_eval \
    --fp16 \
    --learning_rate 3e-4 \
    --max_length_seconds 16 \
    --attention_mask False \
    --warmup_ratio 0.1 \
    --num_train_epochs 10 \
    --per_device_train_batch_size 8 \
    --gradient_accumulation_steps 4 \
    --per_device_eval_batch_size 1 \
    --dataloader_num_workers 8 \
    --logging_strategy steps \
    --logging_steps 10 \
    --evaluation_strategy epoch \
    --save_strategy epoch \
    --load_best_model_at_end True \
    --metric_for_best_model accuracy \
    --save_total_limit 3 \
    --seed 0 \
    --push_to_hub
```

On 4 V100 GPUs (16GB), this script should run in ~1 hour and yield accuracy of **79.45%**.

👀 See the results here: [anton-l/wav2vec2-base-lang-id](https://huggingface.co/anton-l/wav2vec2-base-lang-id)

## Sharing your model on 🤗 Hub

0. If you haven't already, [sign up](https://huggingface.co/join) for a 🤗 account

1. Make sure you have `git-lfs` installed and git set up.

```bash
$ apt install git-lfs
```

2. Log in with your HuggingFace account credentials using `huggingface-cli`

```bash
$ huggingface-cli login
# ...follow the prompts
```

3. When running the script, pass the following arguments:

```bash
python run_audio_classification.py \
    --push_to_hub \
    --hub_model_id <username/model_id> \
    ...
```

### Examples

The following table shows a couple of demonstration fine-tuning runs.
It has been verified that the script works for the following datasets:

- [SUPERB Keyword Spotting](https://huggingface.co/datasets/superb#ks)
- [Common Language](https://huggingface.co/datasets/common_language)

| 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 | [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) |
| Common Language | [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) | 12 | 0.7945 | 4 V100 GPUs | 1h10m  | [here](https://huggingface.co/anton-l/wav2vec2-base-lang-id) |
