Music Spectrogram diffusion pipeline

docs/source/en/_toctree.yml

@@ -158,6 +158,8 @@
       title: Score SDE VE
     - local: api/pipelines/semantic_stable_diffusion
       title: Semantic Guidance
+    - local: api/pipelines/spectrogram_diffusion
+      title: "Spectrogram Diffusion"
     - sections:
       - local: api/pipelines/stable_diffusion/overview
         title: Overview

docs/source/en/api/pipelines/spectrogram_diffusion.mdx

@@ -0,0 +1,54 @@
+<!--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.
+-->
+
+# Multi-instrument Music Synthesis with Spectrogram Diffusion
+
+## Overview
+
+[Spectrogram Diffusion](https://arxiv.org/abs/2206.05408) by Curtis Hawthorne, Ian Simon, Adam Roberts, Neil Zeghidour, Josh Gardner, Ethan Manilow, and Jesse Engel.
+
+An ideal music synthesizer should be both interactive and expressive, generating high-fidelity audio in realtime for arbitrary combinations of instruments and notes. Recent neural synthesizers have exhibited a tradeoff between domain-specific models that offer detailed control of only specific instruments, or raw waveform models that can train on any music but with minimal control and slow generation. In this work, we focus on a middle ground of neural synthesizers that can generate audio from MIDI sequences with arbitrary combinations of instruments in realtime. This enables training on a wide range of transcription datasets with a single model, which in turn offers note-level control of composition and instrumentation across a wide range of instruments. We use a simple two-stage process: MIDI to spectrograms with an encoder-decoder Transformer, then spectrograms to audio with a generative adversarial network (GAN) spectrogram inverter. We compare training the decoder as an autoregressive model and as a Denoising Diffusion Probabilistic Model (DDPM) and find that the DDPM approach is superior both qualitatively and as measured by audio reconstruction and Fréchet distance metrics. Given the interactivity and generality of this approach, we find this to be a promising first step towards interactive and expressive neural synthesis for arbitrary combinations of instruments and notes.
+
+The original codebase of this implementation can be found at [magenta/music-spectrogram-diffusion](https://github.com/magenta/music-spectrogram-diffusion).
+
+## Model
+
+![img](https://storage.googleapis.com/music-synthesis-with-spectrogram-diffusion/architecture.png)
+
+As depicted above the model takes as input a MIDI file and tokenizes it into a sequence of 5 second intervals. Each tokenized interval then together with positional encodings is passed through the Note Encoder and its representation is concatenated with the previous window's generated spectrogram representation obtained via the Context Encoder. For the initial 5 second window this is set to zero. The resulting context is then used as conditioning to sample the denoised Spectrogram from the MIDI window and we concatenate this spectrogram to the final output as well as use it for the context of the next MIDI window. The process repeats till we have gone over all the MIDI inputs. Finally a MelGAN decoder converts the potentially long spectrogram to audio which is the final result of this pipeline.
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Colab
+|---|---|:---:|
+| [pipeline_spectrogram_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/spectrogram_diffusion/pipeline_spectrogram_diffusion) | *Unconditional Audio Generation* | - |
+
+
+## Example usage
+
+```python
+from diffusers import SpectrogramDiffusionPipeline, MidiProcessor
+
+pipe = SpectrogramDiffusionPipeline.from_pretrained("google/music-spectrogram-diffusion")
+pipe = pipe.to("cuda")
+processor = MidiProcessor()
+
+# Download MIDI from: wget http://www.piano-midi.de/midis/beethoven/beethoven_hammerklavier_2.mid
+output = pipe(processor("beethoven_hammerklavier_2.mid"))
+
+audio = output.audios[0]
+```
+
+## SpectrogramDiffusionPipeline
+[[autodoc]] SpectrogramDiffusionPipeline
+	- all
+	- __call__

scripts/convert_music_spectrogram_to_diffusers.py

@@ -0,0 +1,213 @@
+#!/usr/bin/env python3
+import argparse
+import os
+
+import jax as jnp
+import numpy as onp
+import torch
+import torch.nn as nn
+from music_spectrogram_diffusion import inference
+from t5x import checkpoints
+
+from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
+from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, T5FilmDecoder
+
+
+MODEL = "base_with_context"
+
+
+def load_notes_encoder(weights, model):
+    model.token_embedder.weight = nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]))
+    model.position_encoding.weight = nn.Parameter(
+        torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False
+    )
+    for lyr_num, lyr in enumerate(model.encoders):
+        ly_weight = weights[f"layers_{lyr_num}"]
+        lyr.layer[0].layer_norm.weight = nn.Parameter(
+            torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"])
+        )
+
+        attention_weights = ly_weight["attention"]
+        lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
+        lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
+        lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
+        lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
+
+        lyr.layer[1].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
+
+        lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
+        lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
+        lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
+
+    model.layer_norm.weight = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))
+    return model
+
+
+def load_continuous_encoder(weights, model):
+    model.input_proj.weight = nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T))
+
+    model.position_encoding.weight = nn.Parameter(
+        torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False
+    )
+
+    for lyr_num, lyr in enumerate(model.encoders):
+        ly_weight = weights[f"layers_{lyr_num}"]
+        attention_weights = ly_weight["attention"]
+
+        lyr.layer[0].SelfAttention.q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
+        lyr.layer[0].SelfAttention.k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
+        lyr.layer[0].SelfAttention.v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
+        lyr.layer[0].SelfAttention.o.weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
+        lyr.layer[0].layer_norm.weight = nn.Parameter(
+            torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"])
+        )
+
+        lyr.layer[1].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
+        lyr.layer[1].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
+        lyr.layer[1].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
+        lyr.layer[1].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
+
+    model.layer_norm.weight = nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]))
+
+    return model
+
+
+def load_decoder(weights, model):
+    model.conditioning_emb[0].weight = nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T))
+    model.conditioning_emb[2].weight = nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T))
+
+    model.position_encoding.weight = nn.Parameter(
+        torch.FloatTensor(weights["Embed_0"]["embedding"]), requires_grad=False
+    )
+
+    model.continuous_inputs_projection.weight = nn.Parameter(
+        torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T)
+    )
+
+    for lyr_num, lyr in enumerate(model.decoders):
+        ly_weight = weights[f"layers_{lyr_num}"]
+        lyr.layer[0].layer_norm.weight = nn.Parameter(
+            torch.FloatTensor(ly_weight["pre_self_attention_layer_norm"]["scale"])
+        )
+
+        lyr.layer[0].FiLMLayer.scale_bias.weight = nn.Parameter(
+            torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T)
+        )
+
+        attention_weights = ly_weight["self_attention"]
+        lyr.layer[0].attention.to_q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
+        lyr.layer[0].attention.to_k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
+        lyr.layer[0].attention.to_v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
+        lyr.layer[0].attention.to_out[0].weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
+
+        attention_weights = ly_weight["MultiHeadDotProductAttention_0"]
+        lyr.layer[1].attention.to_q.weight = nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T))
+        lyr.layer[1].attention.to_k.weight = nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T))
+        lyr.layer[1].attention.to_v.weight = nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T))
+        lyr.layer[1].attention.to_out[0].weight = nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T))
+        lyr.layer[1].layer_norm.weight = nn.Parameter(
+            torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"])
+        )
+
+        lyr.layer[2].layer_norm.weight = nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]))
+        lyr.layer[2].film.scale_bias.weight = nn.Parameter(
+            torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T)
+        )
+        lyr.layer[2].DenseReluDense.wi_0.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T))
+        lyr.layer[2].DenseReluDense.wi_1.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T))
+        lyr.layer[2].DenseReluDense.wo.weight = nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T))
+
+    model.decoder_norm.weight = nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]))
+
+    model.spec_out.weight = nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T))
+
+    return model
+
+
+def main(args):
+    t5_checkpoint = checkpoints.load_t5x_checkpoint(args.checkpoint_path)
+    t5_checkpoint = jnp.tree_util.tree_map(onp.array, t5_checkpoint)
+
+    gin_overrides = [
+        "from __gin__ import dynamic_registration",
+        "from music_spectrogram_diffusion.models.diffusion import diffusion_utils",
+        "diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0",
+        "diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()",
+    ]
+
+    gin_file = os.path.join(args.checkpoint_path, "..", "config.gin")
+    gin_config = inference.parse_training_gin_file(gin_file, gin_overrides)
+    synth_model = inference.InferenceModel(args.checkpoint_path, gin_config)
+
+    scheduler = DDPMScheduler(beta_schedule="squaredcos_cap_v2", variance_type="fixed_large")
+
+    notes_encoder = SpectrogramNotesEncoder(
+        max_length=synth_model.sequence_length["inputs"],
+        vocab_size=synth_model.model.module.config.vocab_size,
+        d_model=synth_model.model.module.config.emb_dim,
+        dropout_rate=synth_model.model.module.config.dropout_rate,
+        num_layers=synth_model.model.module.config.num_encoder_layers,
+        num_heads=synth_model.model.module.config.num_heads,
+        d_kv=synth_model.model.module.config.head_dim,
+        d_ff=synth_model.model.module.config.mlp_dim,
+        feed_forward_proj="gated-gelu",
+    )
+
+    continuous_encoder = SpectrogramContEncoder(
+        input_dims=synth_model.audio_codec.n_dims,
+        targets_context_length=synth_model.sequence_length["targets_context"],
+        d_model=synth_model.model.module.config.emb_dim,
+        dropout_rate=synth_model.model.module.config.dropout_rate,
+        num_layers=synth_model.model.module.config.num_encoder_layers,
+        num_heads=synth_model.model.module.config.num_heads,
+        d_kv=synth_model.model.module.config.head_dim,
+        d_ff=synth_model.model.module.config.mlp_dim,
+        feed_forward_proj="gated-gelu",
+    )
+
+    decoder = T5FilmDecoder(
+        input_dims=synth_model.audio_codec.n_dims,
+        targets_length=synth_model.sequence_length["targets_context"],
+        max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time,
+        d_model=synth_model.model.module.config.emb_dim,
+        num_layers=synth_model.model.module.config.num_decoder_layers,
+        num_heads=synth_model.model.module.config.num_heads,
+        d_kv=synth_model.model.module.config.head_dim,
+        d_ff=synth_model.model.module.config.mlp_dim,
+        dropout_rate=synth_model.model.module.config.dropout_rate,
+    )
+
+    notes_encoder = load_notes_encoder(t5_checkpoint["target"]["token_encoder"], notes_encoder)
+    continuous_encoder = load_continuous_encoder(t5_checkpoint["target"]["continuous_encoder"], continuous_encoder)
+    decoder = load_decoder(t5_checkpoint["target"]["decoder"], decoder)
+
+    melgan = OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder")
+
+    pipe = SpectrogramDiffusionPipeline(
+        notes_encoder=notes_encoder,
+        continuous_encoder=continuous_encoder,
+        decoder=decoder,
+        scheduler=scheduler,
+        melgan=melgan,
+    )
+    if args.save:
+        pipe.save_pretrained(args.output_path)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.")
+    parser.add_argument(
+        "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
+    )
+    parser.add_argument(
+        "--checkpoint_path",
+        default=f"{MODEL}/checkpoint_500000",
+        type=str,
+        required=False,
+        help="Path to the original jax model checkpoint.",
+    )
+    args = parser.parse_args()
+
+    main(args)

setup.py

@@ -95,8 +95,10 @@
     "Jinja2",
     "k-diffusion>=0.0.12",
     "librosa",
+    "note-seq",
     "numpy",
     "parameterized",
+    "protobuf>=3.20.3,<4",
     "pytest",
     "pytest-timeout",
     "pytest-xdist",
@@ -182,13 +184,14 @@ def run(self):
 extras = {}
 extras["quality"] = deps_list("black", "isort", "ruff", "hf-doc-builder")
 extras["docs"] = deps_list("hf-doc-builder")
-extras["training"] = deps_list("accelerate", "datasets", "tensorboard", "Jinja2")
+extras["training"] = deps_list("accelerate", "datasets", "protobuf", "tensorboard", "Jinja2")
 extras["test"] = deps_list(
     "compel",
     "datasets",
     "Jinja2",
     "k-diffusion",
     "librosa",
+    "note-seq",
     "parameterized",
     "pytest",
     "pytest-timeout",

src/diffusers/__init__.py

@@ -8,6 +8,7 @@
     is_k_diffusion_available,
     is_k_diffusion_version,
     is_librosa_available,
+    is_note_seq_available,
     is_onnx_available,
     is_scipy_available,
     is_torch_available,
@@ -37,6 +38,7 @@
         ControlNetModel,
         ModelMixin,
         PriorTransformer,
+        T5FilmDecoder,
         Transformer2DModel,
         UNet1DModel,
         UNet2DConditionModel,
@@ -172,6 +174,14 @@
 else:
     from .pipelines import AudioDiffusionPipeline, Mel
 
+try:
+    if not (is_torch_available() and is_note_seq_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from .utils.dummy_torch_and_note_seq_objects import *  # noqa F403
+else:
+    from .pipelines import SpectrogramDiffusionPipeline
+
 try:
     if not is_flax_available():
         raise OptionalDependencyNotAvailable()
@@ -205,3 +215,11 @@
         FlaxStableDiffusionInpaintPipeline,
         FlaxStableDiffusionPipeline,
     )
+
+try:
+    if not (is_note_seq_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from .utils.dummy_note_seq_objects import *  # noqa F403
+else:
+    from .pipelines import MidiProcessor

src/diffusers/dependency_versions_table.py

@@ -19,8 +19,10 @@
     "Jinja2": "Jinja2",
     "k-diffusion": "k-diffusion>=0.0.12",
     "librosa": "librosa",
+    "note-seq": "note-seq",
     "numpy": "numpy",
     "parameterized": "parameterized",
+    "protobuf": "protobuf>=3.20.3,<4",
     "pytest": "pytest",
     "pytest-timeout": "pytest-timeout",
     "pytest-xdist": "pytest-xdist",

src/diffusers/models/__init__.py

@@ -21,6 +21,7 @@
     from .dual_transformer_2d import DualTransformer2DModel
     from .modeling_utils import ModelMixin
     from .prior_transformer import PriorTransformer
+    from .t5_film_transformer import T5FilmDecoder
     from .transformer_2d import Transformer2DModel
     from .unet_1d import UNet1DModel
     from .unet_2d import UNet2DModel