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import json
import torch
from parameterized import parameterized
from torchaudio.models.wav2vec2 import wav2vec2_base, wav2vec2_large, wav2vec2_large_lv60k
from torchaudio.models.wav2vec2.utils import import_huggingface_model
from torchaudio_unittest.common_utils import get_asset_path, skipIfNoModule, TorchaudioTestCase
def _load_config(*paths):
with open(f'{get_asset_path("wav2vec2", "huggingface", *paths)}.json', "r") as file_:
return json.load(file_)
def _name_func(testcase_func, i, param):
return f"{testcase_func.__name__}_{i}_{param[0][1].__name__}"
# Pretrained
HF_BASE = _load_config("wav2vec2-base")
HF_LARGE = _load_config("wav2vec2-large")
HF_LARGE_LV60 = _load_config("wav2vec2-large-lv60")
HF_LARGE_XLSR_53 = _load_config("wav2vec2-large-xlsr-53")
HF_BASE_10K_VOXPOPULI = _load_config("wav2vec2-base-10k-voxpopuli")
# Finetuned
HF_BASE_960H = _load_config("wav2vec2-base-960h")
HF_LARGE_960H = _load_config("wav2vec2-large-960h")
HF_LARGE_LV60_960H = _load_config("wav2vec2-large-960h-lv60")
HF_LARGE_LV60_SELF_960H = _load_config("wav2vec2-large-960h-lv60-self")
HF_LARGE_XLSR_DE = _load_config("wav2vec2-large-xlsr-53-german")
# Config and corresponding factory functions
PRETRAIN_CONFIGS = parameterized.expand(
[
(HF_BASE, wav2vec2_base),
(HF_LARGE, wav2vec2_large),
(HF_LARGE_LV60, wav2vec2_large_lv60k),
(HF_LARGE_XLSR_53, wav2vec2_large_lv60k),
(HF_BASE_10K_VOXPOPULI, wav2vec2_base),
],
name_func=_name_func,
)
FINETUNE_CONFIGS = parameterized.expand(
[
(HF_BASE_960H, wav2vec2_base),
(HF_LARGE_960H, wav2vec2_large),
(HF_LARGE_LV60_960H, wav2vec2_large_lv60k),
(HF_LARGE_LV60_SELF_960H, wav2vec2_large_lv60k),
(HF_LARGE_XLSR_DE, wav2vec2_large_lv60k),
],
name_func=_name_func,
)
@skipIfNoModule("transformers")
class TestHFIntegration(TorchaudioTestCase):
"""Test the process of importing the models from Hugging Face Transformers
Test methods in this test suite check the following things
1. Models loaded with Hugging Face Transformers cane be imported.
2. The same model can be recreated without Hugging Face Transformers.
"""
def _get_model(self, config):
# Helper function to avoid importing transformers on module scope.
# Normally, we use `is_module_available` helper function to check if
# the library is available, and import it on module scope if available.
# However, somehow, once "transformers" is imported, `is_module_available`
# starts to fail. Therefore, we defer importing "transformers" until
# the actual tests are started.
from transformers.models.wav2vec2 import Wav2Vec2Config, Wav2Vec2ForCTC, Wav2Vec2Model
if config["architectures"] == ["Wav2Vec2Model"]:
return Wav2Vec2Model(Wav2Vec2Config(**config))
if config["architectures"] == ["Wav2Vec2ForCTC"]:
return Wav2Vec2ForCTC(Wav2Vec2Config(**config))
raise ValueError(f'Unexpected arch: {config["architectures"]}')
def _test_import_pretrain(self, original, imported, config):
# FeatureExtractor
x = torch.randn(3, 1024)
ref = original.feature_extractor(x).transpose(1, 2)
hyp, _ = imported.feature_extractor(x, None)
self.assertEqual(ref, hyp)
# Feature projection
x = torch.randn(3, 10, config["conv_dim"][-1])
ref = original.feature_projection(x)[0]
hyp = imported.encoder.feature_projection(x)
self.assertEqual(ref, hyp)
# Convolutional Positional Encoder
x = torch.randn(3, 256, config["hidden_size"])
ref = original.encoder.pos_conv_embed(x)
hyp = imported.encoder.transformer.pos_conv_embed(x)
self.assertEqual(ref, hyp)
# Encoder Transformer Layer
for original_, imported_ in zip(original.encoder.layers, imported.encoder.transformer.layers):
b, l, e = 16, 3, config["hidden_size"]
x = torch.randn(b, l, e)
mask = torch.randn(b, 1, l, l)
(ref,) = original_(x, attention_mask=mask, output_attentions=False)
hyp = imported_(x, mask)
self.assertEqual(ref, hyp)
# The whole Encoder Transformer
b, l, e = 16, 3, config["hidden_size"]
x = torch.randn(b, l, e)
ref = original.encoder(x).last_hidden_state
hyp = imported.encoder.transformer(x)
self.assertEqual(ref, hyp)
def _test_import_finetune(self, original, imported, config):
# Aux
x = torch.randn(3, 10, config["hidden_size"])
ref = original.lm_head(x)
hyp = imported.aux(x)
self.assertEqual(ref, hyp)
# The whole model without mask
x = torch.randn(3, 1024)
ref = original(x).logits
hyp, _ = imported(x)
self.assertEqual(ref, hyp)
# The whole model without mask
batch_size, num_frames = 3, 1024
x = torch.randn(batch_size, num_frames)
ref = original(x).logits
hyp, _ = imported(x)
self.assertEqual(ref, hyp)
# The whole model with mask
batch_size, num_frames = 3, 1024
x = torch.randn(batch_size, num_frames)
lengths = torch.randint(
low=0,
high=num_frames,
size=[
batch_size,
],
)
mask = torch.arange(num_frames).expand(batch_size, num_frames) < lengths[:, None]
ref = original(x, attention_mask=mask).logits
hyp, output_lengths = imported(x, lengths)
for i, l in enumerate(output_lengths):
self.assertEqual(ref[i, :l, ...], hyp[i, :l, ...])
@PRETRAIN_CONFIGS
def test_import_pretrain(self, config, _):
"""wav2vec2 models from HF transformers can be imported and yields the same results"""
original = self._get_model(config).eval()
imported = import_huggingface_model(original).eval()
self._test_import_pretrain(original, imported, config)
@FINETUNE_CONFIGS
def test_import_finetune(self, config, _):
"""wav2vec2 models from HF transformers can be imported and yields the same results"""
original = self._get_model(config).eval()
imported = import_huggingface_model(original).eval()
self._test_import_pretrain(original.wav2vec2, imported, config)
self._test_import_finetune(original, imported, config)
def _test_recreate(self, imported, reloaded, config):
# FeatureExtractor
x = torch.randn(3, 1024)
ref, _ = imported.feature_extractor(x, None)
hyp, _ = reloaded.feature_extractor(x, None)
self.assertEqual(ref, hyp)
# Feature projection
x = torch.randn(3, 10, config["conv_dim"][-1])
ref = imported.encoder.feature_projection(x)
hyp = reloaded.encoder.feature_projection(x)
self.assertEqual(ref, hyp)
# Convolutional Positional Encoder
x = torch.randn(3, 256, config["hidden_size"])
ref = imported.encoder.transformer.pos_conv_embed(x)
hyp = reloaded.encoder.transformer.pos_conv_embed(x)
self.assertEqual(ref, hyp)
# Encoder Transformer Layer
for imported_, reloaded_ in zip(imported.encoder.transformer.layers, reloaded.encoder.transformer.layers):
b, l, e = 16, 3, config["hidden_size"]
x = torch.randn(b, l, e)
mask = torch.randn(b, 1, l, l)
ref = imported_(x, mask)
hyp = reloaded_(x, mask)
self.assertEqual(ref, hyp)
# The whole Encoder Transformer
# TODO: Add mask pattern. Expected mask shapes and values are different.
b, l, e = 16, 3, config["hidden_size"]
x = torch.randn(b, l, e)
mask = torch.randn(b, 1, l, l)
ref = imported.encoder.transformer(x)
hyp = reloaded.encoder.transformer(x)
self.assertEqual(ref, hyp)
# Aux
if imported.aux is not None:
x = torch.randn(3, 10, config["hidden_size"])
ref = imported.aux(x)
hyp = reloaded.aux(x)
self.assertEqual(ref, hyp)
# The whole model
x = torch.randn(3, 1024)
ref, _ = imported(x)
hyp, _ = reloaded(x)
self.assertEqual(ref, hyp)
@PRETRAIN_CONFIGS
def test_recreate_pretrain(self, config, factory_func):
"""Imported models can be recreated via a factory function without Hugging Face transformers."""
imported = import_huggingface_model(self._get_model(config)).eval()
reloaded = factory_func()
reloaded.load_state_dict(imported.state_dict())
reloaded.eval()
self._test_recreate(imported, reloaded, config)
@FINETUNE_CONFIGS
def test_recreate_finetune(self, config, factory_func):
"""Imported models can be recreated via a factory function without Hugging Face transformers."""
imported = import_huggingface_model(self._get_model(config)).eval()
reloaded = factory_func(aux_num_out=imported.aux.out_features)
reloaded.load_state_dict(imported.state_dict())
reloaded.eval()
self._test_recreate(imported, reloaded, config)
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