Spaces:
Runtime error
Runtime error
File size: 15,342 Bytes
e0097f3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 |
# coding=utf-8
# Copyright 2023 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 gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechT5HifiGan,
SpeechT5HifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class AudioLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = AudioLDMPipeline
params = TEXT_TO_AUDIO_PARAMS
batch_params = TEXT_TO_AUDIO_BATCH_PARAMS
required_optional_params = frozenset(
[
"num_inference_steps",
"num_waveforms_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
]
)
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=(32, 64),
class_embed_type="simple_projection",
projection_class_embeddings_input_dim=32,
class_embeddings_concat=True,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=1,
out_channels=1,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = ClapTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
projection_dim=32,
)
text_encoder = ClapTextModelWithProjection(text_encoder_config)
tokenizer = RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta", model_max_length=77)
vocoder_config = SpeechT5HifiGanConfig(
model_in_dim=8,
sampling_rate=16000,
upsample_initial_channel=16,
upsample_rates=[2, 2],
upsample_kernel_sizes=[4, 4],
resblock_kernel_sizes=[3, 7],
resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]],
normalize_before=False,
)
vocoder = SpeechT5HifiGan(vocoder_config)
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"vocoder": vocoder,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
}
return inputs
def test_audioldm_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(**inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-2
def test_audioldm_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = audioldm_pipe.tokenizer(
prompt,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = audioldm_pipe.text_encoder(
text_inputs,
)
prompt_embeds = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
prompt_embeds = F.normalize(prompt_embeds, dim=-1)
inputs["prompt_embeds"] = prompt_embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm_negative_prompt_embeds(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = audioldm_pipe(**inputs)
audio_1 = output.audios[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
embeds = []
for p in [prompt, negative_prompt]:
text_inputs = audioldm_pipe.tokenizer(
p,
padding="max_length",
max_length=audioldm_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
text_embeds = audioldm_pipe.text_encoder(
text_inputs,
)
text_embeds = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
text_embeds = F.normalize(text_embeds, dim=-1)
embeds.append(text_embeds)
inputs["prompt_embeds"], inputs["negative_prompt_embeds"] = embeds
# forward
output = audioldm_pipe(**inputs)
audio_2 = output.audios[0]
assert np.abs(audio_1 - audio_2).max() < 1e-2
def test_audioldm_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "egg cracking"
output = audioldm_pipe(**inputs, negative_prompt=negative_prompt)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) == 256
audio_slice = audio[:10]
expected_slice = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032]
)
assert np.abs(audio_slice - expected_slice).max() < 1e-2
def test_audioldm_num_waveforms_per_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = "A hammer hitting a wooden surface"
# test num_waveforms_per_prompt=1 (default)
audios = audioldm_pipe(prompt, num_inference_steps=2).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
batch_size = 2
audios = audioldm_pipe([prompt] * batch_size, num_inference_steps=2).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
num_waveforms_per_prompt = 2
audios = audioldm_pipe(prompt, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
batch_size = 2
audios = audioldm_pipe(
[prompt] * batch_size, num_inference_steps=2, num_waveforms_per_prompt=num_waveforms_per_prompt
).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def test_audioldm_audio_length_in_s(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
vocoder_sampling_rate = audioldm_pipe.vocoder.config.sampling_rate
inputs = self.get_dummy_inputs(device)
output = audioldm_pipe(audio_length_in_s=0.016, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.016
output = audioldm_pipe(audio_length_in_s=0.032, **inputs)
audio = output.audios[0]
assert audio.ndim == 1
assert len(audio) / vocoder_sampling_rate == 0.032
def test_audioldm_vocoder_model_in_dim(self):
components = self.get_dummy_components()
audioldm_pipe = AudioLDMPipeline(**components)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
prompt = ["hey"]
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
assert audio_shape == (1, 256)
config = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
audioldm_pipe.vocoder = SpeechT5HifiGan(config).to(torch_device)
output = audioldm_pipe(prompt, num_inference_steps=1)
audio_shape = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def test_attention_slicing_forward_pass(self):
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=False)
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(test_mean_pixel_difference=False)
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_attention_forwardGenerator_pass(self):
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=False)
@slow
class AudioLDMPipelineSlowTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 8, 128, 16))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "A hammer hitting a wooden surface",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 2.5,
}
return inputs
def test_audioldm(self):
audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81920
audio_slice = audio[77230:77240]
expected_slice = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315]
)
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 1e-2
def test_audioldm_lms(self):
audioldm_pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm")
audioldm_pipe.scheduler = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config)
audioldm_pipe = audioldm_pipe.to(torch_device)
audioldm_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
audio = audioldm_pipe(**inputs).audios[0]
assert audio.ndim == 1
assert len(audio) == 81920
audio_slice = audio[27780:27790]
expected_slice = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212])
max_diff = np.abs(expected_slice - audio_slice).max()
assert max_diff < 3e-2
|