mirror of
https://github.com/huggingface/transformers.git
synced 2025-08-02 03:01:07 +06:00
7f91950901
* silly change to allow making a PR * clean up doc comments * simplify hertz_to_mel and mel_to_hertz * fixup * clean up power_to_db * also add amplitude_to_db * move functions * clean up mel_filter_bank * fixup * credit librosa & torchaudio authors * add unit tests * tests for power_to_db and amplitude_to_db * add mel_filter_bank tests * rewrite STFT * add convenience spectrogram function * missing transpose * fewer transposes * add integration test to M-CTC-T * frame length can be either window or FFT length * rewrite stft API * add preemphasis coefficient * move argument * add log option to spectrogram * replace M-CTC-T feature extractor * fix api thing * replace whisper STFT * replace whisper mel filters * replace tvlt's stft * allow alternate window names * replace speecht5 stft * fixup * fix integration tests * fix doc comments * remove manual FFT length calculation * fix docs * go away, deprecation warnings * combine everything into spectrogram function * add deprecated functions back * fixup
307 lines
13 KiB
Python
307 lines
13 KiB
Python
# coding=utf-8
|
|
# Copyright 2022 HuggingFace Inc.
|
|
#
|
|
# 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 itertools
|
|
import random
|
|
import unittest
|
|
|
|
import numpy as np
|
|
|
|
from transformers import is_speech_available
|
|
from transformers.testing_utils import require_torch
|
|
|
|
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
|
|
|
|
|
|
if is_speech_available():
|
|
from transformers import MCTCTFeatureExtractor
|
|
|
|
global_rng = random.Random()
|
|
|
|
|
|
def floats_list(shape, scale=1.0, rng=None, name=None):
|
|
"""Creates a random float32 tensor"""
|
|
if rng is None:
|
|
rng = global_rng
|
|
|
|
values = []
|
|
for _batch_idx in range(shape[0]):
|
|
values.append([])
|
|
for _ in range(shape[1]):
|
|
values[-1].append(rng.random() * scale)
|
|
|
|
return values
|
|
|
|
|
|
@require_torch
|
|
class MCTCTFeatureExtractionTester(unittest.TestCase):
|
|
def __init__(
|
|
self,
|
|
parent,
|
|
batch_size=7,
|
|
min_seq_length=400,
|
|
max_seq_length=2000,
|
|
feature_size=24,
|
|
num_mel_bins=24,
|
|
padding_value=0.0,
|
|
sampling_rate=16_000,
|
|
return_attention_mask=True,
|
|
do_normalize=True,
|
|
):
|
|
self.parent = parent
|
|
self.batch_size = batch_size
|
|
self.min_seq_length = min_seq_length
|
|
self.max_seq_length = max_seq_length
|
|
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
|
|
self.feature_size = feature_size
|
|
self.num_mel_bins = num_mel_bins
|
|
self.padding_value = padding_value
|
|
self.sampling_rate = sampling_rate
|
|
self.return_attention_mask = return_attention_mask
|
|
self.do_normalize = do_normalize
|
|
|
|
def prepare_feat_extract_dict(self):
|
|
return {
|
|
"feature_size": self.feature_size,
|
|
"num_mel_bins": self.num_mel_bins,
|
|
"padding_value": self.padding_value,
|
|
"sampling_rate": self.sampling_rate,
|
|
"return_attention_mask": self.return_attention_mask,
|
|
"do_normalize": self.do_normalize,
|
|
}
|
|
|
|
def prepare_inputs_for_common(self, equal_length=False, numpify=False):
|
|
def _flatten(list_of_lists):
|
|
return list(itertools.chain(*list_of_lists))
|
|
|
|
if equal_length:
|
|
speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
|
|
else:
|
|
# make sure that inputs increase in size
|
|
speech_inputs = [
|
|
floats_list((x, self.feature_size))
|
|
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
|
|
]
|
|
if numpify:
|
|
speech_inputs = [np.asarray(x) for x in speech_inputs]
|
|
return speech_inputs
|
|
|
|
|
|
@require_torch
|
|
class MCTCTFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
|
|
feature_extraction_class = MCTCTFeatureExtractor if is_speech_available() else None
|
|
|
|
def setUp(self):
|
|
self.feat_extract_tester = MCTCTFeatureExtractionTester(self)
|
|
|
|
def _check_zero_mean_unit_variance(self, input_vector):
|
|
self.assertTrue(np.all(np.mean(input_vector) < 1e-3))
|
|
self.assertTrue(np.all(np.abs(np.var(input_vector) - 1) < 1e-3))
|
|
|
|
def test_call(self):
|
|
# Tests that all call wrap to encode_plus and batch_encode_plus
|
|
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
|
|
# create three inputs of length 800, 1000, and 12000
|
|
speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)]
|
|
np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
|
|
|
|
# Test feature size
|
|
input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features
|
|
self.assertTrue(input_features.ndim == 3)
|
|
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
|
|
|
|
# Test not batched input
|
|
encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
|
|
encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
|
|
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
|
|
|
|
# Test batched
|
|
encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
|
|
encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
|
|
for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
|
|
self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
|
|
|
|
def test_cepstral_mean_and_variance_normalization(self):
|
|
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
|
|
speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)]
|
|
|
|
paddings = ["longest", "max_length", "do_not_pad"]
|
|
max_lengths = [None, 16, None]
|
|
for max_length, padding in zip(max_lengths, paddings):
|
|
inputs = feature_extractor(
|
|
speech_inputs,
|
|
padding=padding,
|
|
max_length=max_length,
|
|
return_attention_mask=True,
|
|
truncation=max_length is not None, # reference to #16419
|
|
)
|
|
input_features = inputs.input_features
|
|
attention_mask = inputs.attention_mask
|
|
fbank_feat_lengths = [np.sum(x) for x in attention_mask]
|
|
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
|
|
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
|
|
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])
|
|
|
|
def test_cepstral_mean_and_variance_normalization_np(self):
|
|
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
|
|
speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)]
|
|
|
|
paddings = ["longest", "max_length", "do_not_pad"]
|
|
max_lengths = [None, 16, None]
|
|
for max_length, padding in zip(max_lengths, paddings):
|
|
inputs = feature_extractor(
|
|
speech_inputs,
|
|
max_length=max_length,
|
|
padding=padding,
|
|
return_tensors="np",
|
|
return_attention_mask=True,
|
|
truncation=max_length is not None,
|
|
)
|
|
input_features = inputs.input_features
|
|
attention_mask = inputs.attention_mask
|
|
fbank_feat_lengths = [np.sum(x) for x in attention_mask]
|
|
|
|
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]])
|
|
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6)
|
|
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]])
|
|
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6)
|
|
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]])
|
|
|
|
def test_cepstral_mean_and_variance_normalization_trunc_max_length(self):
|
|
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
|
|
speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)]
|
|
inputs = feature_extractor(
|
|
speech_inputs,
|
|
padding="max_length",
|
|
max_length=4,
|
|
truncation=True,
|
|
return_tensors="np",
|
|
return_attention_mask=True,
|
|
)
|
|
input_features = inputs.input_features
|
|
attention_mask = inputs.attention_mask
|
|
fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
|
|
|
|
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
|
|
self._check_zero_mean_unit_variance(input_features[1])
|
|
self._check_zero_mean_unit_variance(input_features[2])
|
|
|
|
def test_cepstral_mean_and_variance_normalization_trunc_longest(self):
|
|
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
|
|
speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)]
|
|
inputs = feature_extractor(
|
|
speech_inputs,
|
|
padding="longest",
|
|
max_length=4,
|
|
truncation=True,
|
|
return_tensors="np",
|
|
return_attention_mask=True,
|
|
)
|
|
input_features = inputs.input_features
|
|
attention_mask = inputs.attention_mask
|
|
fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
|
|
|
|
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
|
|
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
|
|
self._check_zero_mean_unit_variance(input_features[2])
|
|
|
|
# make sure that if max_length < longest -> then pad to max_length
|
|
self.assertEqual(input_features.shape, (3, 4, 24))
|
|
|
|
speech_inputs = [floats_list((1, x))[0] for x in range(8000, 14000, 2000)]
|
|
inputs = feature_extractor(
|
|
speech_inputs,
|
|
padding="longest",
|
|
max_length=16,
|
|
truncation=True,
|
|
return_tensors="np",
|
|
return_attention_mask=True,
|
|
)
|
|
input_features = inputs.input_features
|
|
attention_mask = inputs.attention_mask
|
|
fbank_feat_lengths = np.sum(attention_mask == 1, axis=1)
|
|
|
|
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]])
|
|
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]])
|
|
self._check_zero_mean_unit_variance(input_features[2])
|
|
|
|
# make sure that if max_length < longest -> then pad to max_length
|
|
self.assertEqual(input_features.shape, (3, 16, 24))
|
|
|
|
def test_double_precision_pad(self):
|
|
import torch
|
|
|
|
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
|
|
np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
|
|
py_speech_inputs = np_speech_inputs.tolist()
|
|
|
|
for inputs in [py_speech_inputs, np_speech_inputs]:
|
|
np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
|
|
self.assertTrue(np_processed.input_features.dtype == np.float32)
|
|
pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
|
|
self.assertTrue(pt_processed.input_features.dtype == torch.float32)
|
|
|
|
def test_different_window(self):
|
|
import torch
|
|
|
|
init_dict = self.feat_extract_tester.prepare_feat_extract_dict()
|
|
init_dict["win_function"] = "hann_window"
|
|
|
|
feature_extractor = self.feature_extraction_class(**init_dict)
|
|
np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
|
|
py_speech_inputs = np_speech_inputs.tolist()
|
|
|
|
for inputs in [py_speech_inputs, np_speech_inputs]:
|
|
np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
|
|
self.assertTrue(np_processed.input_features.dtype == np.float32)
|
|
pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
|
|
self.assertTrue(pt_processed.input_features.dtype == torch.float32)
|
|
|
|
def _load_datasamples(self, num_samples):
|
|
from datasets import load_dataset
|
|
|
|
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
# automatic decoding with librispeech
|
|
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
|
|
|
|
return [x["array"] for x in speech_samples]
|
|
|
|
def test_integration(self):
|
|
# fmt: off
|
|
expected = np.array([
|
|
[
|
|
1.1280, 1.1319, 1.2744, 1.4369, 1.4328, 1.3671, 1.2889, 1.3046,
|
|
1.4419, 0.8387, 0.2995, 0.0404, 0.1068, 0.0472, 0.3728, 1.3356,
|
|
1.4491, 0.4770, 0.3997, 0.2776, 0.3184, -0.1243, -0.1170, -0.0828
|
|
],
|
|
[
|
|
1.0826, 1.0565, 1.2110, 1.3886, 1.3416, 1.2009, 1.1894, 1.2707,
|
|
1.5153, 0.7005, 0.4916, 0.4017, 0.3743, 0.1935, 0.4228, 1.1084,
|
|
0.9768, 0.0608, 0.2044, 0.1723, 0.0433, -0.2360, -0.2478, -0.2643
|
|
],
|
|
[
|
|
1.0590, 0.9923, 1.1185, 1.3309, 1.1971, 1.0067, 1.0080, 1.2036,
|
|
1.5397, 1.0383, 0.7672, 0.7551, 0.4878, 0.8771, 0.7565, 0.8775,
|
|
0.9042, 0.4595, 0.6157, 0.4954, 0.1857, 0.0307, 0.0199, 0.1033
|
|
],
|
|
])
|
|
# fmt: on
|
|
|
|
input_speech = self._load_datasamples(1)
|
|
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
|
|
input_features = feature_extractor(input_speech, sampling_rate=16000, return_tensors="pt").input_features
|
|
self.assertTrue(np.allclose(input_features[0, 100:103], expected, atol=1e-4))
|