Add MusicGen Melody (#28819)
* first modeling code * make repository * still WIP * update model * add tests * add latest change * clean docstrings and copied from * update docstrings md and readme * correct chroma function * correct copied from and remove unreleated test * add doc to toctree * correct imports * add convert script to notdoctested * Add suggestion from Sanchit Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * correct get_uncoditional_inputs docstrings * modify README according to SANCHIT feedback * add chroma to audio utils * clean librosa and torchaudio hard dependencies * fix FE * refactor audio decoder -> audio encoder for consistency with previous musicgen * refactor conditional -> encoder * modify sampling rate logics * modify license at the beginning * refactor all_self_attns->all_attentions * remove ignore copy from causallm generate * add copied from for from_sub_models * fix make copies * add warning if audio is truncated * add copied from where relevant * remove artefact * fix convert script * fix torchaudio and FE * modify chroma method according to feedback-> better naming * refactor input_values->input_features * refactor input_values->input_features and fix import fe * add input_features to docstrigs * correct inputs_embeds logics * remove dtype conversion * refactor _prepare_conditional_hidden_states_kwargs_for_generation ->_prepare_encoder_hidden_states_kwargs_for_generation * change warning for chroma length * Update src/transformers/models/musicgen_melody/convert_musicgen_melody_transformers.py Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> * change way to save wav, using soundfile * correct docs and change to soundfile * fix import * fix init proj layers * remove line breaks from md * fix issue with docstrings * add FE suggestions * improve is in logics and remove useless imports * remove custom from_pretrained * simplify docstring code * add suggestions for modeling tests * make style * update converting script with sanity check * remove encoder attention mask from conditional generation * replace musicgen melody checkpoints with official orga * rename ylacombe->facebook in checkpoints * fix copies * remove unecessary warning * add shape in code docstrings * add files to slow doc tests * fix md bug and add md to not_tested * make fix-copies * fix hidden states test and batching --------- Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
This commit is contained in:
Yoach Lacombe
0
tests/models/musicgen_melody/__init__.py
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0
tests/models/musicgen_melody/__init__.py
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# coding=utf-8
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# Copyright 2024 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import itertools
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import math
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import os
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import random
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import tempfile
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import unittest
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import numpy as np
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from transformers.testing_utils import (
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check_json_file_has_correct_format,
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require_torch,
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require_torchaudio,
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)
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from transformers.utils.import_utils import is_torchaudio_available
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from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
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if is_torchaudio_available():
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import torch
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from transformers import MusicgenMelodyFeatureExtractor
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global_rng = random.Random()
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# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
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def floats_list(shape, scale=1.0, rng=None, name=None):
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"""Creates a random float32 tensor"""
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if rng is None:
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rng = global_rng
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values = []
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for batch_idx in range(shape[0]):
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values.append([])
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for _ in range(shape[1]):
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values[-1].append(rng.random() * scale)
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return values
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# Copied from tests.models.musicgen.test_modeling_musicgen.get_bip_bip
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def get_bip_bip(bip_duration=0.125, duration=0.5, sample_rate=32000):
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"""Produces a series of 'bip bip' sounds at a given frequency."""
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timesteps = np.arange(int(duration * sample_rate)) / sample_rate
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wav = np.cos(2 * math.pi * 440 * timesteps)
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time_period = (timesteps % (2 * bip_duration)) / (2 * bip_duration)
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envelope = time_period >= 0.5
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return wav * envelope
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@require_torch
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@require_torchaudio
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class MusicgenMelodyFeatureExtractionTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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batch_size=7,
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min_seq_length=400,
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max_seq_length=2000,
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feature_size=12,
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padding_value=0.0,
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sampling_rate=4_000,
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return_attention_mask=True,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.min_seq_length = min_seq_length
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self.max_seq_length = max_seq_length
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self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
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self.padding_value = padding_value
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self.sampling_rate = sampling_rate
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self.return_attention_mask = return_attention_mask
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self.feature_size = feature_size
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self.num_chroma = feature_size
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def prepare_feat_extract_dict(self):
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return {
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"feature_size": self.feature_size,
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"padding_value": self.padding_value,
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"sampling_rate": self.sampling_rate,
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"return_attention_mask": self.return_attention_mask,
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}
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# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTester.prepare_inputs_for_common
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def prepare_inputs_for_common(self, equal_length=False, numpify=False):
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def _flatten(list_of_lists):
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return list(itertools.chain(*list_of_lists))
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if equal_length:
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speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
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else:
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# make sure that inputs increase in size
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speech_inputs = [
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floats_list((x, self.feature_size))
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for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
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]
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if numpify:
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speech_inputs = [np.asarray(x) for x in speech_inputs]
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return speech_inputs
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@require_torchaudio
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@require_torch
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class MusicgenMelodyFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
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feature_extraction_class = MusicgenMelodyFeatureExtractor if is_torchaudio_available() else None
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def setUp(self):
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self.feat_extract_tester = MusicgenMelodyFeatureExtractionTester(self)
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# Copied from tests.models.seamless_m4t.test_feature_extraction_seamless_m4t.SeamlessM4TFeatureExtractionTest.test_feat_extract_from_and_save_pretrained
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def test_feat_extract_from_and_save_pretrained(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = feat_extract_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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feat_extract_second = self.feature_extraction_class.from_pretrained(tmpdirname)
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dict_first = feat_extract_first.to_dict()
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dict_second = feat_extract_second.to_dict()
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self.assertDictEqual(dict_first, dict_second)
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# Copied from tests.models.seamless_m4t.test_feature_extraction_seamless_m4t.SeamlessM4TFeatureExtractionTest.test_feat_extract_to_json_file
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def test_feat_extract_to_json_file(self):
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feat_extract_first = self.feature_extraction_class(**self.feat_extract_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "feat_extract.json")
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feat_extract_first.to_json_file(json_file_path)
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feat_extract_second = self.feature_extraction_class.from_json_file(json_file_path)
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dict_first = feat_extract_first.to_dict()
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dict_second = feat_extract_second.to_dict()
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self.assertEqual(dict_first, dict_second)
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def test_call(self):
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# Tests that all call wrap to encode_plus and batch_encode_plus
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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# create three inputs of length 800, 1000, and 1200
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speech_inputs = [floats_list((1, x))[0] for x in range(800, 1400, 200)]
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np_speech_inputs = [np.asarray(speech_input) for speech_input in speech_inputs]
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# Test feature size
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input_features = feature_extractor(np_speech_inputs, padding=True, return_tensors="np").input_features
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self.assertTrue(input_features.ndim == 3)
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self.assertTrue(input_features.shape[0] == 3)
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# Ignore copy
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self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
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# Test not batched input
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encoded_sequences_1 = feature_extractor(speech_inputs[0], return_tensors="np").input_features
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encoded_sequences_2 = feature_extractor(np_speech_inputs[0], return_tensors="np").input_features
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self.assertTrue(np.allclose(encoded_sequences_1, encoded_sequences_2, atol=1e-3))
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# Test batched
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encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
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encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
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for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
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self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
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# Test 2-D numpy arrays are batched.
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speech_inputs = [floats_list((1, x))[0] for x in (800, 800, 800)]
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np_speech_inputs = np.asarray(speech_inputs)
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encoded_sequences_1 = feature_extractor(speech_inputs, return_tensors="np").input_features
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encoded_sequences_2 = feature_extractor(np_speech_inputs, return_tensors="np").input_features
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for enc_seq_1, enc_seq_2 in zip(encoded_sequences_1, encoded_sequences_2):
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self.assertTrue(np.allclose(enc_seq_1, enc_seq_2, atol=1e-3))
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@require_torchaudio
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def test_call_from_demucs(self):
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# Tests that all call wrap to encode_plus and batch_encode_plus
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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# (batch_size, num_stems, channel_size, audio_length)
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inputs = torch.rand([4, 5, 2, 44000])
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# Test feature size
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input_features = feature_extractor(inputs, padding=True, return_tensors="np").input_features
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self.assertTrue(input_features.ndim == 3)
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self.assertTrue(input_features.shape[0] == 4)
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self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size)
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# Test single input
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encoded_sequences_1 = feature_extractor(inputs[[0]], return_tensors="np").input_features
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self.assertTrue(np.allclose(encoded_sequences_1[0], input_features[0], atol=1e-3))
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# Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest.test_double_precision_pad with input_features->input_features
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def test_double_precision_pad(self):
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import torch
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feature_extractor = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
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np_speech_inputs = np.random.rand(100, 32).astype(np.float64)
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py_speech_inputs = np_speech_inputs.tolist()
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for inputs in [py_speech_inputs, np_speech_inputs]:
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np_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="np")
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self.assertTrue(np_processed.input_features.dtype == np.float32)
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pt_processed = feature_extractor.pad([{"input_features": inputs}], return_tensors="pt")
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self.assertTrue(pt_processed.input_features.dtype == torch.float32)
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def test_integration(self):
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EXPECTED_INPUT_FEATURES = torch.zeros([2, 8, 12])
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EXPECTED_INPUT_FEATURES[0, :6, 9] = 1
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EXPECTED_INPUT_FEATURES[0, 6:, 0] = 1
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EXPECTED_INPUT_FEATURES[1, :, 9] = 1
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input_speech = [get_bip_bip(duration=0.5), get_bip_bip(duration=1.0)]
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feature_extractor = MusicgenMelodyFeatureExtractor()
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input_features = feature_extractor(input_speech, return_tensors="pt").input_features
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self.assertEqual(input_features.shape, (2, 8, 12))
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self.assertTrue((input_features == EXPECTED_INPUT_FEATURES).all())
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1449
tests/models/musicgen_melody/test_modeling_musicgen_melody.py
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1449
tests/models/musicgen_melody/test_modeling_musicgen_melody.py
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File diff suppressed because it is too large
Load Diff
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tests/models/musicgen_melody/test_processor_musicgen_melody.py
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tests/models/musicgen_melody/test_processor_musicgen_melody.py
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# Copyright 2024 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for the MusicGen processor."""
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import random
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import shutil
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import tempfile
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import unittest
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import numpy as np
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from transformers import T5Tokenizer, T5TokenizerFast
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from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio
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from transformers.utils.import_utils import is_torchaudio_available
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if is_torchaudio_available():
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from transformers import MusicgenMelodyFeatureExtractor, MusicgenMelodyProcessor
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global_rng = random.Random()
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# Copied from tests.models.whisper.test_feature_extraction_whisper.floats_list
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def floats_list(shape, scale=1.0, rng=None, name=None):
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"""Creates a random float32 tensor"""
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if rng is None:
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rng = global_rng
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values = []
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for batch_idx in range(shape[0]):
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values.append([])
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for _ in range(shape[1]):
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values[-1].append(rng.random() * scale)
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return values
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@require_torch
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@require_sentencepiece
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@require_torchaudio
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# Copied from tests.models.musicgen.test_processing_musicgen.MusicgenProcessorTest with Musicgen->MusicgenMelody, Encodec->MusicgenMelody, padding_mask->attention_mask, input_values->input_features
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class MusicgenMelodyProcessorTest(unittest.TestCase):
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def setUp(self):
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# Ignore copy
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self.checkpoint = "facebook/musicgen-melody"
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self.tmpdirname = tempfile.mkdtemp()
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def get_tokenizer(self, **kwargs):
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return T5Tokenizer.from_pretrained(self.checkpoint, **kwargs)
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def get_feature_extractor(self, **kwargs):
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return MusicgenMelodyFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def test_save_load_pretrained_default(self):
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tokenizer = self.get_tokenizer()
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feature_extractor = self.get_feature_extractor()
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processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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processor.save_pretrained(self.tmpdirname)
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processor = MusicgenMelodyProcessor.from_pretrained(self.tmpdirname)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
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self.assertIsInstance(processor.tokenizer, T5TokenizerFast)
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
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self.assertIsInstance(processor.feature_extractor, MusicgenMelodyFeatureExtractor)
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def test_save_load_pretrained_additional_features(self):
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processor = MusicgenMelodyProcessor(
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tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor()
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)
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processor.save_pretrained(self.tmpdirname)
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tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
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feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
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processor = MusicgenMelodyProcessor.from_pretrained(
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self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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self.assertIsInstance(processor.tokenizer, T5TokenizerFast)
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.feature_extractor, MusicgenMelodyFeatureExtractor)
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def test_feature_extractor(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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raw_speech = floats_list((3, 1000))
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input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
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input_processor = processor(raw_speech, return_tensors="np")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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def test_tokenizer(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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input_str = "This is a test string"
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encoded_processor = processor(text=input_str)
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encoded_tok = tokenizer(input_str)
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for key in encoded_tok.keys():
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self.assertListEqual(encoded_tok[key], encoded_processor[key])
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def test_tokenizer_decode(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
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decoded_processor = processor.batch_decode(sequences=predicted_ids)
|
||||
decoded_tok = tokenizer.batch_decode(predicted_ids)
|
||||
|
||||
self.assertListEqual(decoded_tok, decoded_processor)
|
||||
|
||||
def test_model_input_names(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
self.assertListEqual(
|
||||
processor.model_input_names,
|
||||
feature_extractor.model_input_names,
|
||||
msg="`processor` and `feature_extractor` model input names do not match",
|
||||
)
|
||||
|
||||
# Ignore copy
|
||||
def test_decode_audio(self):
|
||||
feature_extractor = self.get_feature_extractor(padding_side="left")
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = MusicgenMelodyProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
attention_mask = np.zeros((3, 20))
|
||||
attention_mask[0, -5:] = 1
|
||||
attention_mask[1, -20:] = 1
|
||||
attention_mask[2, -10:] = 1
|
||||
|
||||
generated_speech = np.asarray(floats_list((3, 20)))[:, None, :]
|
||||
decoded_audios = processor.batch_decode(generated_speech, attention_mask=attention_mask)
|
||||
|
||||
self.assertIsInstance(decoded_audios, list)
|
||||
|
||||
for audio in decoded_audios:
|
||||
self.assertIsInstance(audio, np.ndarray)
|
||||
|
||||
self.assertTrue(decoded_audios[0].shape == (1, 5))
|
||||
self.assertTrue(decoded_audios[1].shape == (1, 20))
|
||||
self.assertTrue(decoded_audios[2].shape == (1, 10))
|
||||
Reference in New Issue
Block a user