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transformers/tests/models/dia/test_feature_extraction_dia.py Normal file
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# Copyright 2025 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.
"""Tests for the Dia feature extractor."""
import itertools
import random
import unittest
import numpy as np
from transformers import DiaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
global_rng = random.Random()
# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
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 DiaFeatureExtractionTester:
# Copied from tests.models.dac.test_feature_extraction_dac.DacFeatureExtractionTester.__init__
def __init__(
self,
parent,
batch_size=7,
min_seq_length=400,
max_seq_length=2000,
feature_size=1,
padding_value=0.0,
sampling_rate=16000,
hop_length=512,
):
self.parent = parent
self.batch_size = batch_size
self.min_seq_length = min_seq_length
self.max_seq_length = max_seq_length
self.hop_length = hop_length
self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
self.feature_size = feature_size
self.padding_value = padding_value
self.sampling_rate = sampling_rate
# Copied from tests.models.dac.test_feature_extraction_dac.DacFeatureExtractionTester.prepare_feat_extract_dict
def prepare_feat_extract_dict(self):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"hop_length": self.hop_length,
}
# Copied from tests.models.encodec.test_feature_extraction_encodec.EnCodecFeatureExtractionTester.prepare_inputs_for_common
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:
audio_inputs = floats_list((self.batch_size, self.max_seq_length))
else:
# make sure that inputs increase in size
audio_inputs = [
_flatten(floats_list((x, self.feature_size)))
for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
]
if numpify:
audio_inputs = [np.asarray(x) for x in audio_inputs]
return audio_inputs
@require_torch
class DiaFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
feature_extraction_class = DiaFeatureExtractor
def setUp(self):
self.feat_extract_tester = DiaFeatureExtractionTester(self)
# Copied from tests.models.dac.test_feature_extraction_dac.DacFeatureExtractionTest.test_call
def test_call(self):
# Tests that all call wrap to encode_plus and batch_encode_plus
feat_extract = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
# create three inputs of length 800, 1000, and 1200
audio_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
np_audio_inputs = [np.asarray(audio_input) for audio_input in audio_inputs]
# Test not batched input
encoded_sequences_1 = feat_extract(audio_inputs[0], return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_audio_inputs[0], return_tensors="np").input_values
self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
# Test batched
encoded_sequences_1 = feat_extract(audio_inputs, padding=True, return_tensors="np").input_values
encoded_sequences_2 = feat_extract(np_audio_inputs, padding=True, return_tensors="np").input_values
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))
# Copied from tests.models.dac.test_feature_extraction_dac.DacFeatureExtractionTest.test_double_precision_pad
def test_double_precision_pad(self):
feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
np_audio_inputs = np.random.rand(100).astype(np.float64)
py_audio_inputs = np_audio_inputs.tolist()
for inputs in [py_audio_inputs, np_audio_inputs]:
np_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="np")
self.assertTrue(np_processed.input_values.dtype == np.float32)
pt_processed = feature_extractor.pad([{"input_values": inputs}], return_tensors="pt")
self.assertTrue(pt_processed.input_values.dtype == torch.float32)
# Copied from tests.models.dac.test_feature_extraction_dac.DacFeatureExtractionTest._load_datasamples
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
audio_samples = ds.sort("id")[:num_samples]["audio"]
return [x["array"] for x in audio_samples]
# Copied from tests.models.dac.test_feature_extraction_dac.DacFeatureExtractionTest.test_integration with Dac->Dia
def test_integration(self):
# fmt: off
EXPECTED_INPUT_VALUES = torch.tensor(
[ 2.3803711e-03, 2.0751953e-03, 1.9836426e-03, 2.1057129e-03,
1.6174316e-03, 3.0517578e-04, 9.1552734e-05, 3.3569336e-04,
9.7656250e-04, 1.8310547e-03, 2.0141602e-03, 2.1057129e-03,
1.7395020e-03, 4.5776367e-04, -3.9672852e-04, 4.5776367e-04,
1.0070801e-03, 9.1552734e-05, 4.8828125e-04, 1.1596680e-03,
7.3242188e-04, 9.4604492e-04, 1.8005371e-03, 1.8310547e-03,
8.8500977e-04, 4.2724609e-04, 4.8828125e-04, 7.3242188e-04,
1.0986328e-03, 2.1057129e-03]
)
# fmt: on
input_audio = self._load_datasamples(1)
feature_extractor = DiaFeatureExtractor()
input_values = feature_extractor(input_audio, return_tensors="pt")["input_values"]
self.assertEqual(input_values.shape, (1, 1, 93696))
torch.testing.assert_close(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, rtol=1e-4, atol=1e-4)
audio_input_end = torch.tensor(input_audio[0][-30:], dtype=torch.float32)
torch.testing.assert_close(input_values[0, 0, -46:-16], audio_input_end, rtol=1e-4, atol=1e-4)
def test_integration_stereo(self):
# fmt: off
EXPECTED_INPUT_VALUES = torch.tensor(
[2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]
)
# fmt: on
input_audio = self._load_datasamples(1)
input_audio = [np.tile(input_audio[0][None], reps=(2, 1))]
feature_extractor = DiaFeatureExtractor(feature_size=2)
input_values = feature_extractor(input_audio, return_tensors="pt").input_values
self.assertEqual(input_values.shape, (1, 1, 93696))
torch.testing.assert_close(input_values[0, 0, :30], EXPECTED_INPUT_VALUES, rtol=1e-4, atol=1e-4)
# Copied from tests.models.dac.test_feature_extraction_dac.DacFeatureExtractionTest.test_truncation_and_padding with Dac->Dia
def test_truncation_and_padding(self):
input_audio = self._load_datasamples(2)
# would be easier if the stride was like
feature_extractor = DiaFeatureExtractor()
# pad and trunc raise an error ?
with self.assertRaisesRegex(
ValueError,
"^Both padding and truncation were set. Make sure you only set one.$",
):
truncated_outputs = feature_extractor(
input_audio, padding="max_length", truncation=True, return_tensors="pt"
).input_values
# force truncate to max_length
truncated_outputs = feature_extractor(
input_audio, truncation=True, max_length=48000, return_tensors="pt"
).input_values
self.assertEqual(truncated_outputs.shape, (2, 1, 48128))
# pad:
padded_outputs = feature_extractor(input_audio, padding=True, return_tensors="pt").input_values
self.assertEqual(padded_outputs.shape, (2, 1, 93696))
# force pad to max length
truncated_outputs = feature_extractor(
input_audio, padding="max_length", max_length=100000, return_tensors="pt"
).input_values
self.assertEqual(truncated_outputs.shape, (2, 1, 100352))
# force no pad
with self.assertRaisesRegex(
ValueError,
"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",
):
truncated_outputs = feature_extractor(input_audio, padding=False, return_tensors="pt").input_values
truncated_outputs = feature_extractor(input_audio[0], padding=False, return_tensors="pt").input_values
self.assertEqual(truncated_outputs.shape, (1, 1, 93680))