# What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

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## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
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      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

add VITS model. # What does this PR do?

Adds the VITS model for text-to-speech, in particular to support the MMS-TTS checkpoints (which use the same model architecture but a different tokenizer).

<!--
Congratulations! You've made it this far! You're not quite done yet though.

Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

Then, please replace this with a description of the change and which issue is fixed (if applicable). Please also include relevant motivation and context. List any dependencies (if any) that are required for this change.

Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

<!-- Remove if not applicable -->

Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [x] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# Copyright 2023 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.

import unittest

import numpy as np

from transformers import (
    MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING,
    AutoProcessor,
    TextToAudioPipeline,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
    slow,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
class TextToAudioPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING
    # for now only text_to_waveform and not text_to_spectrogram

    @slow
    @require_torch
    def test_small_model_pt(self):
        speech_generator = pipeline(task="text-to-audio", model="facebook/musicgen-small", framework="pt")

        forward_params = {
            "do_sample": False,
            "max_new_tokens": 250,
        }

        outputs = speech_generator("This is a test", forward_params=forward_params)

        # musicgen sampling_rate is not straightforward to get
        self.assertIsNone(outputs["sampling_rate"])

        audio = outputs["audio"]

        self.assertEqual(ANY(np.ndarray), audio)

        # test two examples side-by-side
        outputs = speech_generator(["This is a test", "This is a second test"], forward_params=forward_params)

        audio = [output["audio"] for output in outputs]

        self.assertEqual([ANY(np.ndarray), ANY(np.ndarray)], audio)

        # test batching
        outputs = speech_generator(
            ["This is a test", "This is a second test"], forward_params=forward_params, batch_size=2
        )

        self.assertEqual(ANY(np.ndarray), outputs[0]["audio"])

    @slow
    @require_torch
    def test_large_model_pt(self):
        speech_generator = pipeline(task="text-to-audio", model="suno/bark-small", framework="pt")

        # test text-to-speech

        forward_params = {
            # Using `do_sample=False` to force deterministic output
            "do_sample": False,
            "semantic_max_new_tokens": 100,
        }

        outputs = speech_generator("This is a test", forward_params=forward_params)

        self.assertEqual(
            {"audio": ANY(np.ndarray), "sampling_rate": 24000},
            outputs,
        )

        # test two examples side-by-side
        outputs = speech_generator(
            ["This is a test", "This is a second test"],
            forward_params=forward_params,
        )

        audio = [output["audio"] for output in outputs]

        self.assertEqual([ANY(np.ndarray), ANY(np.ndarray)], audio)

        # test other generation strategy

        forward_params = {
            "do_sample": True,
            "semantic_max_new_tokens": 100,
            "semantic_num_return_sequences": 2,
        }

        outputs = speech_generator("This is a test", forward_params=forward_params)

        audio = outputs["audio"]

        self.assertEqual(ANY(np.ndarray), audio)

        # test using a speaker embedding
        processor = AutoProcessor.from_pretrained("suno/bark-small")
        temp_inp = processor("hey, how are you?", voice_preset="v2/en_speaker_5")
        history_prompt = temp_inp["history_prompt"]
        forward_params["history_prompt"] = history_prompt

        outputs = speech_generator(
            ["This is a test", "This is a second test"],
            forward_params=forward_params,
            batch_size=2,
        )

        audio = [output["audio"] for output in outputs]

        self.assertEqual([ANY(np.ndarray), ANY(np.ndarray)], audio)

    @slow
    @require_torch_gpu
    def test_conversion_additional_tensor(self):
        speech_generator = pipeline(task="text-to-audio", model="suno/bark-small", framework="pt", device=0)
        processor = AutoProcessor.from_pretrained("suno/bark-small")

        forward_params = {
            "do_sample": True,
            "semantic_max_new_tokens": 100,
        }

        # atm, must do to stay coherent with BarkProcessor
        preprocess_params = {
            "max_length": 256,
            "add_special_tokens": False,
            "return_attention_mask": True,
            "return_token_type_ids": False,
            "padding": "max_length",
        }

        outputs = speech_generator(
            "This is a test",
            forward_params=forward_params,
            preprocess_params=preprocess_params,
        )

        temp_inp = processor("hey, how are you?", voice_preset="v2/en_speaker_5")
        history_prompt = temp_inp["history_prompt"]
        forward_params["history_prompt"] = history_prompt

        # history_prompt is a torch.Tensor passed as a forward_param
        # if generation is successfull, it means that it was passed to the right device
        outputs = speech_generator(
            "This is a test", forward_params=forward_params, preprocess_params=preprocess_params
        )

        self.assertEqual(
            {"audio": ANY(np.ndarray), "sampling_rate": 24000},
            outputs,
        )

    def get_test_pipeline(self, model, tokenizer, processor):
        speech_generator = TextToAudioPipeline(model=model, tokenizer=tokenizer)
        return speech_generator, ["This is a test", "Another test"]

    def run_pipeline_test(self, speech_generator, _):
        outputs = speech_generator("This is a test")

        self.assertEqual(ANY(np.ndarray), outputs["audio"])

        forward_params = {"num_return_sequences": 2, "do_sample": True}

        outputs = speech_generator(["This is great !", "Something else"], forward_params=forward_params)
        audio = [output["audio"] for output in outputs]

        self.assertEqual([ANY(np.ndarray), ANY(np.ndarray)], audio)

# Copyright 2023 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.

import unittest

import numpy as np

from transformers import (
    MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING,
    AutoProcessor,
    TextToAudioPipeline,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
    slow,
)

from .test_pipelines_common import ANY


@is_pipeline_test
@require_torch_or_tf
class TextToAudioPipelineTests(unittest.TestCase):
    model_mapping = MODEL_FOR_TEXT_TO_WAVEFORM_MAPPING
    # for now only text_to_waveform and not text_to_spectrogram

    @slow
    @require_torch
    def test_small_model_pt(self):
        speech_generator = pipeline(task="text-to-audio", model="facebook/musicgen-small", framework="pt")

        forward_params = {
            "do_sample": False,
            "max_new_tokens": 250,
        }

        outputs = speech_generator("This is a test", forward_params=forward_params)

        # musicgen sampling_rate is not straightforward to get
        self.assertIsNone(outputs["sampling_rate"])

        audio = outputs["audio"]

        self.assertEqual(ANY(np.ndarray), audio)

        # test two examples side-by-side
        outputs = speech_generator(["This is a test", "This is a second test"], forward_params=forward_params)

        audio = [output["audio"] for output in outputs]

        self.assertEqual([ANY(np.ndarray), ANY(np.ndarray)], audio)

        # test batching
        outputs = speech_generator(
            ["This is a test", "This is a second test"], forward_params=forward_params, batch_size=2
        )

        self.assertEqual(ANY(np.ndarray), outputs[0]["audio"])

    @slow
    @require_torch
    def test_large_model_pt(self):
        speech_generator = pipeline(task="text-to-audio", model="suno/bark-small", framework="pt")

        # test text-to-speech

        forward_params = {
            # Using `do_sample=False` to force deterministic output
            "do_sample": False,
            "semantic_max_new_tokens": 100,
        }

        outputs = speech_generator("This is a test", forward_params=forward_params)

        self.assertEqual(
            {"audio": ANY(np.ndarray), "sampling_rate": 24000},
            outputs,
        )

        # test two examples side-by-side
        outputs = speech_generator(
            ["This is a test", "This is a second test"],
            forward_params=forward_params,
        )

        audio = [output["audio"] for output in outputs]

        self.assertEqual([ANY(np.ndarray), ANY(np.ndarray)], audio)

        # test other generation strategy

        forward_params = {
            "do_sample": True,
            "semantic_max_new_tokens": 100,
            "semantic_num_return_sequences": 2,
        }

        outputs = speech_generator("This is a test", forward_params=forward_params)

        audio = outputs["audio"]

        self.assertEqual(ANY(np.ndarray), audio)

        # test using a speaker embedding
        processor = AutoProcessor.from_pretrained("suno/bark-small")
        temp_inp = processor("hey, how are you?", voice_preset="v2/en_speaker_5")
        history_prompt = temp_inp["history_prompt"]
        forward_params["history_prompt"] = history_prompt

        outputs = speech_generator(
            ["This is a test", "This is a second test"],
            forward_params=forward_params,
            batch_size=2,
        )

        audio = [output["audio"] for output in outputs]

        self.assertEqual([ANY(np.ndarray), ANY(np.ndarray)], audio)

    @slow
    @require_torch_gpu
    def test_conversion_additional_tensor(self):
        speech_generator = pipeline(task="text-to-audio", model="suno/bark-small", framework="pt", device=0)
        processor = AutoProcessor.from_pretrained("suno/bark-small")

        forward_params = {
            "do_sample": True,
            "semantic_max_new_tokens": 100,
        }

        # atm, must do to stay coherent with BarkProcessor
        preprocess_params = {
            "max_length": 256,
            "add_special_tokens": False,
            "return_attention_mask": True,
            "return_token_type_ids": False,
            "padding": "max_length",
        }

        outputs = speech_generator(
            "This is a test",
            forward_params=forward_params,
            preprocess_params=preprocess_params,
        )

        temp_inp = processor("hey, how are you?", voice_preset="v2/en_speaker_5")
        history_prompt = temp_inp["history_prompt"]
        forward_params["history_prompt"] = history_prompt

        # history_prompt is a torch.Tensor passed as a forward_param
        # if generation is successfull, it means that it was passed to the right device
        outputs = speech_generator(
            "This is a test", forward_params=forward_params, preprocess_params=preprocess_params
        )

        self.assertEqual(
            {"audio": ANY(np.ndarray), "sampling_rate": 24000},
            outputs,
        )

    def get_test_pipeline(self, model, tokenizer, processor):
        speech_generator = TextToAudioPipeline(model=model, tokenizer=tokenizer)
        return speech_generator, ["This is a test", "Another test"]

    def run_pipeline_test(self, speech_generator, _):
        outputs = speech_generator("This is a test")

        self.assertEqual(ANY(np.ndarray), outputs["audio"])

        forward_params = {"num_return_sequences": 2, "do_sample": True}

        outputs = speech_generator(["This is great !", "Something else"], forward_params=forward_params)
        audio = [output["audio"] for output in outputs]

        self.assertEqual([ANY(np.ndarray), ANY(np.ndarray)], audio)
