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# coding=utf-8
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# Copyright 2022 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 gc
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import unittest
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import numpy as np
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import torch
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from MuseVdiffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
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from MuseVdiffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, T5FilmDecoder
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from MuseVdiffusers.utils.testing_utils import (
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enable_full_determinism,
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nightly,
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require_note_seq,
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require_onnxruntime,
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require_torch_gpu,
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skip_mps,
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torch_device,
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)
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from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
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from ..test_pipelines_common import PipelineTesterMixin
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enable_full_determinism()
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MIDI_FILE = "./tests/fixtures/elise_format0.mid"
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# The note-seq package throws an error on import because the default installed version of Ipython
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# is not compatible with python 3.8 which we run in the CI.
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# https://github.com/huggingface/diffusers/actions/runs/4830121056/jobs/8605954838#step:7:98
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@unittest.skip("The note-seq package currently throws an error on import")
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class SpectrogramDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = SpectrogramDiffusionPipeline
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required_optional_params = PipelineTesterMixin.required_optional_params - {
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"callback",
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"latents",
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"callback_steps",
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"output_type",
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"num_images_per_prompt",
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}
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test_attention_slicing = False
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batch_params = TOKENS_TO_AUDIO_GENERATION_PARAMS
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params = TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS
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def get_dummy_components(self):
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torch.manual_seed(0)
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notes_encoder = SpectrogramNotesEncoder(
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max_length=2048,
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vocab_size=1536,
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d_model=768,
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dropout_rate=0.1,
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num_layers=1,
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num_heads=1,
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d_kv=4,
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d_ff=2048,
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feed_forward_proj="gated-gelu",
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)
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continuous_encoder = SpectrogramContEncoder(
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input_dims=128,
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targets_context_length=256,
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d_model=768,
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dropout_rate=0.1,
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num_layers=1,
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num_heads=1,
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d_kv=4,
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d_ff=2048,
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feed_forward_proj="gated-gelu",
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)
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decoder = T5FilmDecoder(
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input_dims=128,
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targets_length=256,
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max_decoder_noise_time=20000.0,
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d_model=768,
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num_layers=1,
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num_heads=1,
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d_kv=4,
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d_ff=2048,
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dropout_rate=0.1,
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)
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scheduler = DDPMScheduler()
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components = {
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"notes_encoder": notes_encoder.eval(),
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"continuous_encoder": continuous_encoder.eval(),
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"decoder": decoder.eval(),
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"scheduler": scheduler,
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"melgan": None,
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}
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return components
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def get_dummy_inputs(self, device, seed=0):
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if str(device).startswith("mps"):
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generator = torch.manual_seed(seed)
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else:
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generator = torch.Generator(device=device).manual_seed(seed)
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inputs = {
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"input_tokens": [
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[1134, 90, 1135, 1133, 1080, 112, 1132, 1080, 1133, 1079, 133, 1132, 1079, 1133, 1] + [0] * 2033
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],
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"generator": generator,
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"num_inference_steps": 4,
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"output_type": "mel",
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}
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return inputs
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def test_spectrogram_diffusion(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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pipe = SpectrogramDiffusionPipeline(**components)
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = pipe(**inputs)
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mel = output.audios
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mel_slice = mel[0, -3:, -3:]
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assert mel_slice.shape == (3, 3)
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expected_slice = np.array(
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[-11.512925, -4.788215, -0.46172905, -2.051715, -10.539147, -10.970963, -9.091634, 4.0, 4.0]
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)
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assert np.abs(mel_slice.flatten() - expected_slice).max() < 1e-2
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@skip_mps
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def test_save_load_local(self):
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return super().test_save_load_local()
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@skip_mps
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def test_dict_tuple_outputs_equivalent(self):
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return super().test_dict_tuple_outputs_equivalent()
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@skip_mps
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def test_save_load_optional_components(self):
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return super().test_save_load_optional_components()
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@skip_mps
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def test_attention_slicing_forward_pass(self):
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return super().test_attention_slicing_forward_pass()
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def test_inference_batch_single_identical(self):
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pass
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def test_inference_batch_consistent(self):
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pass
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@skip_mps
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def test_progress_bar(self):
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return super().test_progress_bar()
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@nightly
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@require_torch_gpu
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@require_onnxruntime
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@require_note_seq
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class PipelineIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def test_callback(self):
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# TODO - test that pipeline can decode tokens in a callback
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# so that music can be played live
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device = torch_device
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pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
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melgan = pipe.melgan
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pipe.melgan = None
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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def callback(step, mel_output):
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# decode mel to audio
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audio = melgan(input_features=mel_output.astype(np.float32))[0]
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assert len(audio[0]) == 81920 * (step + 1)
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# simulate that audio is played
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return audio
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processor = MidiProcessor()
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input_tokens = processor(MIDI_FILE)
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input_tokens = input_tokens[:3]
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generator = torch.manual_seed(0)
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pipe(input_tokens, num_inference_steps=5, generator=generator, callback=callback, output_type="mel")
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def test_spectrogram_fast(self):
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device = torch_device
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pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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processor = MidiProcessor()
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input_tokens = processor(MIDI_FILE)
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# just run two denoising loops
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input_tokens = input_tokens[:2]
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generator = torch.manual_seed(0)
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output = pipe(input_tokens, num_inference_steps=2, generator=generator)
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audio = output.audios[0]
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assert abs(np.abs(audio).sum() - 3612.841) < 1e-1
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def test_spectrogram(self):
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device = torch_device
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pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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processor = MidiProcessor()
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input_tokens = processor(MIDI_FILE)
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# just run 4 denoising loops
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input_tokens = input_tokens[:4]
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generator = torch.manual_seed(0)
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output = pipe(input_tokens, num_inference_steps=100, generator=generator)
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audio = output.audios[0]
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assert abs(np.abs(audio).sum() - 9389.1111) < 5e-2
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