<reponame>rbracco/thunder-speech<filename>tests/wav2vec/test_module_w2v.py
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

# Copyright (c) 2021 scart97

from string import ascii_lowercase

import pytest

import pytorch_lightning as pl
import torch

from tests.utils import mark_slow, requirescuda
from thunder.data.datamodule import ManifestDatamodule
from thunder.wav2vec.module import (
    TextTransformConfig,
    Wav2Vec2Module,
    Wav2Vec2Scriptable,
)


@mark_slow
@requirescuda
def test_dev_run_train(sample_manifest):
    module = Wav2Vec2Module(TextTransformConfig(list(ascii_lowercase)))
    data = ManifestDatamodule(
        train_manifest=sample_manifest,
        val_manifest=sample_manifest,
        test_manifest=sample_manifest,
        num_workers=0,
    )
    trainer = pl.Trainer(
        fast_dev_run=True, logger=False, checkpoint_callback=False, gpus=-1
    )
    trainer.fit(module, datamodule=data)


@mark_slow
def test_predict():
    module = Wav2Vec2Module(TextTransformConfig(list(ascii_lowercase)))
    fake_input = torch.randn(1, 16000)
    fake_transcription = module.predict(fake_input)
    assert isinstance(fake_transcription, list)
    assert len(fake_transcription) == 1
    assert isinstance(fake_transcription[0], str)


@mark_slow
def test_script_module():
    module = Wav2Vec2Module(TextTransformConfig(list(ascii_lowercase)))
    module.eval()
    torchaudio_module = Wav2Vec2Scriptable(module)
    torchaudio_module.eval()
    scripted = torch.jit.script(torchaudio_module)

    fake_input = torch.randn(1, 16000)
    fake_transcription = module.predict(fake_input)
    torchaudio_transcription = torchaudio_module.predict(fake_input)
    scripted_transcription = scripted.predict(fake_input)

    assert fake_transcription[0] == torchaudio_transcription[0]
    assert fake_transcription[0] == scripted_transcription[0]


@pytest.mark.xfail
@mark_slow
def test_quantized_script_module():
    # TODO: check why the quantized model is creating different predictions
    # I'm not sure if that's expected or a bug
    module = Wav2Vec2Module(TextTransformConfig(list(ascii_lowercase)))
    module.eval()
    torchaudio_module = Wav2Vec2Scriptable(module, quantized=True)
    torchaudio_module.eval()

    fake_input = torch.randn(1, 16000)
    fake_transcription = module.predict(fake_input)
    torchaudio_transcription = torchaudio_module.predict(fake_input)

    assert fake_transcription[0] == torchaudio_transcription[0]

# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

# Copyright (c) 2021 scart97

from string import ascii_lowercase

import pytest

import pytorch_lightning as pl
import torch

from tests.utils import mark_slow, requirescuda
from thunder.data.datamodule import ManifestDatamodule
from thunder.wav2vec.module import (
    TextTransformConfig,
    Wav2Vec2Module,
    Wav2Vec2Scriptable,
)


@mark_slow
@requirescuda
def test_dev_run_train(sample_manifest):
    module = Wav2Vec2Module(TextTransformConfig(list(ascii_lowercase)))
    data = ManifestDatamodule(
        train_manifest=sample_manifest,
        val_manifest=sample_manifest,
        test_manifest=sample_manifest,
        num_workers=0,
    )
    trainer = pl.Trainer(
        fast_dev_run=True, logger=False, checkpoint_callback=False, gpus=-1
    )
    trainer.fit(module, datamodule=data)


@mark_slow
def test_predict():
    module = Wav2Vec2Module(TextTransformConfig(list(ascii_lowercase)))
    fake_input = torch.randn(1, 16000)
    fake_transcription = module.predict(fake_input)
    assert isinstance(fake_transcription, list)
    assert len(fake_transcription) == 1
    assert isinstance(fake_transcription[0], str)


@mark_slow
def test_script_module():
    module = Wav2Vec2Module(TextTransformConfig(list(ascii_lowercase)))
    module.eval()
    torchaudio_module = Wav2Vec2Scriptable(module)
    torchaudio_module.eval()
    scripted = torch.jit.script(torchaudio_module)

    fake_input = torch.randn(1, 16000)
    fake_transcription = module.predict(fake_input)
    torchaudio_transcription = torchaudio_module.predict(fake_input)
    scripted_transcription = scripted.predict(fake_input)

    assert fake_transcription[0] == torchaudio_transcription[0]
    assert fake_transcription[0] == scripted_transcription[0]


@pytest.mark.xfail
@mark_slow
def test_quantized_script_module():
    # TODO: check why the quantized model is creating different predictions
    # I'm not sure if that's expected or a bug
    module = Wav2Vec2Module(TextTransformConfig(list(ascii_lowercase)))
    module.eval()
    torchaudio_module = Wav2Vec2Scriptable(module, quantized=True)
    torchaudio_module.eval()

    fake_input = torch.randn(1, 16000)
    fake_transcription = module.predict(fake_input)
    torchaudio_transcription = torchaudio_module.predict(fake_input)

    assert fake_transcription[0] == torchaudio_transcription[0]
# This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. # Copyright (c) 2021 scart97 # TODO: check why the quantized model is creating different predictions # I'm not sure if that's expected or a bug