Add wav2vec2.0 model

docs/source/models.rst

@@ -33,6 +33,29 @@ The models subpackage contains definitions of models for addressing common audio
   .. automethod:: forward
 
 
+
+:hidden:`Wav2Vec2.0`
+~~~~~~~~~~~~~~~~~~~~
+
+Model
+-----
+
+.. autoclass:: Wav2Vec2Model
+
+  .. automethod:: extract_features
+
+  .. automethod:: forward
+
+Factory Functions
+-----------------
+
+.. autofunction:: wav2vec2_base
+
+.. autofunction:: wav2vec2_large
+
+.. autofunction:: wav2vec2_large_lv60k
+
+
 :hidden:`WaveRNN`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 

test/torchaudio_unittest/common_utils/__init__.py

@@ -19,6 +19,7 @@
     skipIfNoKaldi,
     skipIfNoSox,
     skipIfRocm,
+    skipIfNoQengine,
 )
 from .wav_utils import (
     get_wav_data,
@@ -49,6 +50,7 @@
     'skipIfNoSox',
     'skipIfNoSoxBackend',
     'skipIfRocm',
+    'skipIfNoQengine',
     'get_wav_data',
     'normalize_wav',
     'load_wav',

test/torchaudio_unittest/common_utils/case_utils.py

@@ -109,3 +109,7 @@ def skipIfNoModule(module, display_name=None):
 skipIfNoKaldi = unittest.skipIf(not is_kaldi_available(), reason='Kaldi not available')
 skipIfRocm = unittest.skipIf(os.getenv('TORCHAUDIO_TEST_WITH_ROCM', '0') == '1',
                              reason="test doesn't currently work on the ROCm stack")
+skipIfNoQengine = unittest.skipIf(
+    'fbgemm' not in torch.backends.quantized.supported_engines,
+    reason="`fbgemm` is not available."
+)

test/torchaudio_unittest/models/wav2vec2/model_test.py

@@ -0,0 +1,182 @@
+import io
+import torch
+import torch.nn.functional as F
+
+from torchaudio.models.wav2vec2 import (
+    wav2vec2_base,
+    wav2vec2_large,
+    wav2vec2_large_lv60k,
+)
+from torchaudio_unittest.common_utils import (
+    TorchaudioTestCase,
+    skipIfNoQengine,
+    skipIfNoCuda,
+)
+from parameterized import parameterized
+
+factory_funcs = parameterized.expand([
+    (wav2vec2_base, ),
+    (wav2vec2_large, ),
+    (wav2vec2_large_lv60k, ),
+])
+
+
+class TestWav2Vec2Model(TorchaudioTestCase):
+    def _smoke_test(self, device, dtype):
+        model = wav2vec2_base(num_out=32)
+        model = model.to(device=device, dtype=dtype)
+        model = model.eval()
+
+        torch.manual_seed(0)
+        batch_size, num_frames = 3, 1024
+
+        waveforms = torch.randn(
+            batch_size, num_frames, device=device, dtype=dtype)
+        lengths = torch.randint(
+            low=0, high=num_frames, size=[batch_size, ], device=device)
+
+        model(waveforms, lengths)
+
+    @parameterized.expand([(torch.float32, ), (torch.float64, )])
+    def test_cpu_smoke_test(self, dtype):
+        self._smoke_test(torch.device('cpu'), dtype)
+
+    @parameterized.expand([(torch.float32, ), (torch.float64, )])
+    @skipIfNoCuda
+    def test_cuda_smoke_test(self, dtype):
+        self._smoke_test(torch.device('cuda'), dtype)
+
+    @factory_funcs
+    def test_feature_extractor_smoke_test(self, factory_func):
+        """`extract_features` method does not fail"""
+        batch_size, num_frames = 3, 1024
+
+        model = factory_func(num_out=32).eval()
+
+        torch.manual_seed(0)
+        waveforms = torch.randn(batch_size, num_frames)
+        lengths = torch.randint(low=0, high=num_frames, size=[batch_size, ])
+
+        features, lengths = model.extract_features(waveforms, lengths)
+        assert features.ndim == 3
+        assert features.shape[0] == batch_size
+        assert lengths.shape == torch.Size([batch_size])
+
+    @factory_funcs
+    def test_batch_consistency(self, factory_func):
+        """Results from sigle process and batched process should be reasonably close
+        """
+        batch_size, max_frames = 5, 5 * 1024
+
+        model = factory_func(num_out=32).eval()
+
+        torch.manual_seed(0)
+        waveforms = torch.randn(batch_size, max_frames)
+        input_lengths = torch.tensor([i * 3200 for i in range(1, 6)])
+
+        # Batch process with lengths
+        batch_logits, output_lengths = model(waveforms, input_lengths)
+        for i in range(batch_size):
+            # Par-sample process without feeding length
+            single_logit, _ = model(waveforms[i:i + 1, :input_lengths[i]], None)
+            batch_logit = batch_logits[i:i + 1, :output_lengths[i]]
+
+            # Convert to probability so that it's easier to interpretate the diff
+            single_prob = F.softmax(single_logit, dim=2)
+            batch_prob = F.softmax(batch_logit, dim=2)
+            # We allow max atol=0.005 -> 0.5%
+            self.assertEqual(single_prob, batch_prob, atol=0.005, rtol=0)
+
+    @factory_funcs
+    def test_zero_length(self, factory_func):
+        """Passing zero length should not fail"""
+        model = factory_func(num_out=32).eval()
+
+        torch.manual_seed(0)
+        batch_size = 3
+        waveforms = torch.randn(batch_size, 1024)
+        input_lengths = torch.zeros(batch_size)
+        _, output_lengths = model(waveforms, input_lengths)
+        self.assertEqual(torch.zeros_like(output_lengths), output_lengths)
+
+    @factory_funcs
+    def test_torchscript(self, factory_func):
+        """Wav2Vec2Model should be scriptable"""
+        batch_size, num_frames = 3, 1024
+
+        model = factory_func(num_out=32).eval()
+
+        torch.manual_seed(0)
+        waveforms = torch.randn(batch_size, num_frames)
+        lengths = torch.randint(low=0, high=num_frames, size=[batch_size, ])
+
+        ref_out, ref_len = model(waveforms, lengths)
+
+        # TODO: put this in a common method of Mixin class.
+        # Script
+        scripted = torch.jit.script(model)
+        buffer_ = io.BytesIO()
+        torch.jit.save(scripted, buffer_)
+        buffer_.seek(0)
+        scripted = torch.jit.load(buffer_)
+
+        hyp_out, hyp_len = scripted(waveforms, lengths)
+
+        self.assertEqual(hyp_out, ref_out)
+        self.assertEqual(hyp_len, ref_len)
+
+    @factory_funcs
+    @skipIfNoQengine
+    def test_quantize(self, factory_func):
+        """Wav2Vec2Model should support basic quantization"""
+        batch_size, num_frames = 3, 1024
+
+        model = factory_func(num_out=32).eval()
+
+        # Remove the weight normalization forward hook
+        model.encoder.transformer.pos_conv_embed.__prepare_scriptable__()
+        quantized = torch.quantization.quantize_dynamic(
+            model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8)
+
+        # A lazy way to check that Modules are different
+        assert str(quantized) != str(model), "Dynamic quantization did not modify the module."
+
+        torch.manual_seed(0)
+        waveforms = torch.randn(batch_size, num_frames)
+        lengths = torch.randint(low=0, high=num_frames, size=[batch_size, ])
+
+        _, _ = quantized(waveforms, lengths)
+
+    @factory_funcs
+    @skipIfNoQengine
+    def test_quantize_torchscript(self, factory_func):
+        """Quantized Wav2Vec2Model should be scriptable"""
+        batch_size, num_frames = 3, 1024
+
+        model = factory_func(num_out=32).eval()
+
+        # Remove the weight normalization forward hook
+        model.encoder.transformer.pos_conv_embed.__prepare_scriptable__()
+        quantized = torch.quantization.quantize_dynamic(
+            model, qconfig_spec={torch.nn.Linear}, dtype=torch.qint8)
+
+        # A lazy way to check that Modules are different
+        assert str(quantized) != str(model), "Dynamic quantization did not modify the module."
+
+        torch.manual_seed(0)
+        waveforms = torch.randn(batch_size, num_frames)
+        lengths = torch.randint(low=0, high=num_frames, size=[batch_size, ])
+
+        ref_out, ref_len = quantized(waveforms, lengths)
+
+        # Script
+        scripted = torch.jit.script(quantized)
+        buffer_ = io.BytesIO()
+        torch.jit.save(scripted, buffer_)
+        buffer_.seek(0)
+        scripted = torch.jit.load(buffer_)
+
+        hyp_out, hyp_len = scripted(waveforms, lengths)
+
+        self.assertEqual(hyp_out, ref_out)
+        self.assertEqual(hyp_len, ref_len)

torchaudio/models/__init__.py

@@ -2,10 +2,21 @@
 from .wavernn import WaveRNN
 from .conv_tasnet import ConvTasNet
 from .deepspeech import DeepSpeech
+from .wav2vec2 import (
+    Wav2Vec2Model,
+    wav2vec2_base,
+    wav2vec2_large,
+    wav2vec2_large_lv60k,
+)
+
 
 __all__ = [
     'Wav2Letter',
     'WaveRNN',
     'ConvTasNet',
     'DeepSpeech',
+    'Wav2Vec2Model',
+    'wav2vec2_base',
+    'wav2vec2_large',
+    'wav2vec2_large_lv60k',
 ]

torchaudio/models/wav2vec2/__init__.py

@@ -0,0 +1,13 @@
+from .model import (
+    Wav2Vec2Model,
+    wav2vec2_base,
+    wav2vec2_large,
+    wav2vec2_large_lv60k,
+)
+
+__all__ = [
+    'Wav2Vec2Model',
+    'wav2vec2_base',
+    'wav2vec2_large',
+    'wav2vec2_large_lv60k',
+]