Add Musicgen (#24109)
* Add Audiocraft * add cross attention * style * add for lm * convert and verify * introduce t5 * split configs * load t5 + lm * clean conversion * copy from t5 * style * start pattern provider * make generation work * style * fix pos embs * propagate shape changes * propagate shape changes * style * delay pattern: pad tokens at end * audiocraft -> musicgen * fix inits * add mdx * style * fix pad token in processor * override generate and add todos * add init to test * undo pattern delay mask after gen * remove cfg logits processor * remove cfg logits processor * remove logits processor in favour of mask * clean pos embs * make fix copies * update readmes * clean pos emb * refactor encoder/decoder * make fix copies * update conversion * fix config imports * update config docs * make style * send pattern mask to device * pattern mask with delay * recover prompted audio tokens * fix docstrings * laydown test file * pattern edge case * remove t5 ref * add processing class * config refactor * better pattern comment * check if mask is not present * check if mask is not present * refactor to auto class * remove encoder configs * fix processor * processor import * start updating conversion * start updating tests * make style * convert t5, encodec, lm * convert as composite * also convert processor * run generate * classifier free gen * comments and clean up * make style * docs for logit proc * docstring for uncond gen * start lm tests * work tests * let the lm generate * refactor: reshape inside forward * undo greedy loop changes * from_enc_dec -> from_sub_model * fix input id shapes in docstrings * Apply suggestions from code review Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * undo generate changes * from sub model config * Update src/transformers/models/musicgen/modeling_musicgen.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * make generate work again * generate uncond -> get uncond inputs * remove prefix allowed tokens fn * better error message * logit proc checks * Apply suggestions from code review Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> * make decoder only tests work * composite fast tests * make style * uncond generation * feat extr padding * make audio prompt work * fix inputs docstrings * unconditional inputs: dict -> model output * clean up tests * more clean up tests * make style * t5 encoder -> auto text encoder * remove comments * deal with frames * fix auto text * slow tests * nice mdx * remove can generate * todo - hub id * convert m/l * make fix copies * only import generation with torch * ignore decoder from tests * don't wrap uncond inputs * make style * cleaner uncond inputs * add example to musicgen forward * fix docs * ignore MusicGen Model/ForConditionalGeneration in auto mapping * add doc section to toctree * add to doc tests * add processor tests * fix push to hub in conversion * tips for decoder only loading * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * fix conversion for s / m / l checkpoints * import stopping criteria from module * remove from pipeline tests * fix uncond docstring * decode audio method * fix docs * org: sanchit-gandhi -> facebook * fix max pos embeddings * remove auto doc (not compatible with shapes) * bump max pos emb * make style * fix doc * fix config doc * fix config doc * ignore musicgen config from docstring * make style * fix config * fix config for doctest * consistent from_sub_models * don't automap decoder * fix mdx save audio file * fix mdx save audio file * processor batch decode for audio * remove keys to ignore * update doc md * update generation config * allow changes for default generation config * update tests * make style * fix docstring for uncond * fix processor test * fix processor test --------- Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com> Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Sanchit Gandhi
0
tests/models/musicgen/__init__.py
Normal file
0
tests/models/musicgen/__init__.py
Normal file
1346
tests/models/musicgen/test_modeling_musicgen.py
Normal file
1346
tests/models/musicgen/test_modeling_musicgen.py
Normal file
File diff suppressed because it is too large
Load Diff
173
tests/models/musicgen/test_processing_musicgen.py
Normal file
173
tests/models/musicgen/test_processing_musicgen.py
Normal file
@@ -0,0 +1,173 @@
|
||||
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# 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 MusicGen processor."""
|
||||
|
||||
import random
|
||||
import shutil
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
|
||||
from transformers import T5Tokenizer, T5TokenizerFast
|
||||
from transformers.testing_utils import require_sentencepiece, require_torch
|
||||
from transformers.utils.import_utils import is_speech_available, is_torch_available
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
pass
|
||||
|
||||
if is_speech_available():
|
||||
from transformers import EncodecFeatureExtractor, MusicgenProcessor
|
||||
|
||||
|
||||
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
|
||||
@require_sentencepiece
|
||||
class MusicgenProcessorTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.checkpoint = "facebook/musicgen-small"
|
||||
self.tmpdirname = tempfile.mkdtemp()
|
||||
|
||||
def get_tokenizer(self, **kwargs):
|
||||
return T5Tokenizer.from_pretrained(self.checkpoint, **kwargs)
|
||||
|
||||
def get_feature_extractor(self, **kwargs):
|
||||
return EncodecFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
|
||||
|
||||
def tearDown(self):
|
||||
shutil.rmtree(self.tmpdirname)
|
||||
|
||||
def test_save_load_pretrained_default(self):
|
||||
tokenizer = self.get_tokenizer()
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
|
||||
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
processor = MusicgenProcessor.from_pretrained(self.tmpdirname)
|
||||
|
||||
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
|
||||
self.assertIsInstance(processor.tokenizer, T5TokenizerFast)
|
||||
|
||||
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
|
||||
self.assertIsInstance(processor.feature_extractor, EncodecFeatureExtractor)
|
||||
|
||||
def test_save_load_pretrained_additional_features(self):
|
||||
processor = MusicgenProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
|
||||
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
|
||||
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
|
||||
|
||||
processor = MusicgenProcessor.from_pretrained(
|
||||
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
|
||||
)
|
||||
|
||||
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
|
||||
self.assertIsInstance(processor.tokenizer, T5TokenizerFast)
|
||||
|
||||
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
|
||||
self.assertIsInstance(processor.feature_extractor, EncodecFeatureExtractor)
|
||||
|
||||
def test_feature_extractor(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
raw_speech = floats_list((3, 1000))
|
||||
|
||||
input_feat_extract = feature_extractor(raw_speech, return_tensors="np")
|
||||
input_processor = processor(raw_speech, return_tensors="np")
|
||||
|
||||
for key in input_feat_extract.keys():
|
||||
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
|
||||
|
||||
def test_tokenizer(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
input_str = "This is a test string"
|
||||
|
||||
encoded_processor = processor(text=input_str)
|
||||
|
||||
encoded_tok = tokenizer(input_str)
|
||||
|
||||
for key in encoded_tok.keys():
|
||||
self.assertListEqual(encoded_tok[key], encoded_processor[key])
|
||||
|
||||
def test_tokenizer_decode(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
|
||||
|
||||
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 = MusicgenProcessor(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",
|
||||
)
|
||||
|
||||
def test_decode_audio(self):
|
||||
feature_extractor = self.get_feature_extractor(padding_side="left")
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = MusicgenProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
raw_speech = [floats_list((1, x))[0] for x in range(5, 20, 5)]
|
||||
padding_mask = processor(raw_speech).padding_mask
|
||||
|
||||
generated_speech = np.asarray(floats_list((3, 20)))[:, None, :]
|
||||
decoded_audios = processor.batch_decode(generated_speech, padding_mask=padding_mask)
|
||||
|
||||
self.assertIsInstance(decoded_audios, list)
|
||||
|
||||
for audio in decoded_audios:
|
||||
self.assertIsInstance(audio, np.ndarray)
|
||||
|
||||
self.assertTrue(decoded_audios[0].shape == (1, 10))
|
||||
self.assertTrue(decoded_audios[1].shape == (1, 15))
|
||||
self.assertTrue(decoded_audios[2].shape == (1, 20))
|
||||
Reference in New Issue
Block a user