Upload 4 files
Browse files- Dockerfile +2 -0
- app.py +295 -0
- packages.txt +4 -0
- requirements.txt +18 -0
Dockerfile
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RUN echo "deb http://deb.debian.org/debian buster main" >> /etc/apt/sources.list && \
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apt-get update
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app.py
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import os
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os.system("pip install gradio==2.4.6")
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import gradio as gr
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from pathlib import Path
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os.system("pip install gsutil")
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os.system("git clone --branch=main https://github.com/google-research/t5x")
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os.system("mv t5x t5x_tmp; mv t5x_tmp/* .; rm -r t5x_tmp")
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os.system("sed -i 's:jax\[tpu\]:jax:' setup.py")
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os.system("python3 -m pip install -e .")
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# install mt3
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os.system("git clone --branch=main https://github.com/magenta/mt3")
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os.system("mv mt3 mt3_tmp; mv mt3_tmp/* .; rm -r mt3_tmp")
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os.system("python3 -m pip install -e .")
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# copy checkpoints
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os.system("gsutil -q -m cp -r gs://mt3/checkpoints .")
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# copy soundfont (originally from https://sites.google.com/site/soundfonts4u)
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os.system("gsutil -q -m cp gs://magentadata/soundfonts/SGM-v2.01-Sal-Guit-Bass-V1.3.sf2 .")
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#@title Imports and Definitions
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import functools
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import os
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import numpy as np
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import tensorflow.compat.v2 as tf
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import functools
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import gin
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import jax
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import librosa
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import note_seq
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import seqio
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import t5
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import t5x
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from mt3 import metrics_utils
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from mt3 import models
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from mt3 import network
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from mt3 import note_sequences
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from mt3 import preprocessors
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from mt3 import spectrograms
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from mt3 import vocabularies
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import nest_asyncio
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nest_asyncio.apply()
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SAMPLE_RATE = 16000
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SF2_PATH = 'SGM-v2.01-Sal-Guit-Bass-V1.3.sf2'
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def upload_audio(audio, sample_rate):
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return note_seq.audio_io.wav_data_to_samples_librosa(
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audio, sample_rate=sample_rate)
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class InferenceModel(object):
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"""Wrapper of T5X model for music transcription."""
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def __init__(self, checkpoint_path, model_type='mt3'):
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# Model Constants.
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if model_type == 'ismir2021':
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num_velocity_bins = 127
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self.encoding_spec = note_sequences.NoteEncodingSpec
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self.inputs_length = 512
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elif model_type == 'mt3':
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num_velocity_bins = 1
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self.encoding_spec = note_sequences.NoteEncodingWithTiesSpec
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self.inputs_length = 256
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else:
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raise ValueError('unknown model_type: %s' % model_type)
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gin_files = ['/home/user/app/mt3/gin/model.gin',
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'/home/user/app/mt3/gin/mt3.gin']
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self.batch_size = 8
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self.outputs_length = 1024
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self.sequence_length = {'inputs': self.inputs_length,
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'targets': self.outputs_length}
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self.partitioner = t5x.partitioning.ModelBasedPjitPartitioner(
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model_parallel_submesh=(1, 1, 1, 1), num_partitions=1)
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# Build Codecs and Vocabularies.
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self.spectrogram_config = spectrograms.SpectrogramConfig()
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self.codec = vocabularies.build_codec(
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vocab_config=vocabularies.VocabularyConfig(
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num_velocity_bins=num_velocity_bins))
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self.vocabulary = vocabularies.vocabulary_from_codec(self.codec)
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self.output_features = {
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'inputs': seqio.ContinuousFeature(dtype=tf.float32, rank=2),
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'targets': seqio.Feature(vocabulary=self.vocabulary),
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}
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# Create a T5X model.
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self._parse_gin(gin_files)
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self.model = self._load_model()
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# Restore from checkpoint.
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self.restore_from_checkpoint(checkpoint_path)
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@property
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def input_shapes(self):
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return {
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'encoder_input_tokens': (self.batch_size, self.inputs_length),
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'decoder_input_tokens': (self.batch_size, self.outputs_length)
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}
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def _parse_gin(self, gin_files):
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"""Parse gin files used to train the model."""
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gin_bindings = [
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'from __gin__ import dynamic_registration',
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'from mt3 import vocabularies',
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'VOCAB_CONFIG=@vocabularies.VocabularyConfig()',
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'vocabularies.VocabularyConfig.num_velocity_bins=%NUM_VELOCITY_BINS'
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]
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with gin.unlock_config():
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gin.parse_config_files_and_bindings(
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gin_files, gin_bindings, finalize_config=False)
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def _load_model(self):
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"""Load up a T5X `Model` after parsing training gin config."""
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model_config = gin.get_configurable(network.T5Config)()
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module = network.Transformer(config=model_config)
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return models.ContinuousInputsEncoderDecoderModel(
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module=module,
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input_vocabulary=self.output_features['inputs'].vocabulary,
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output_vocabulary=self.output_features['targets'].vocabulary,
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optimizer_def=t5x.adafactor.Adafactor(decay_rate=0.8, step_offset=0),
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input_depth=spectrograms.input_depth(self.spectrogram_config))
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def restore_from_checkpoint(self, checkpoint_path):
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"""Restore training state from checkpoint, resets self._predict_fn()."""
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train_state_initializer = t5x.utils.TrainStateInitializer(
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optimizer_def=self.model.optimizer_def,
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init_fn=self.model.get_initial_variables,
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input_shapes=self.input_shapes,
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partitioner=self.partitioner)
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restore_checkpoint_cfg = t5x.utils.RestoreCheckpointConfig(
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path=checkpoint_path, mode='specific', dtype='float32')
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train_state_axes = train_state_initializer.train_state_axes
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self._predict_fn = self._get_predict_fn(train_state_axes)
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self._train_state = train_state_initializer.from_checkpoint_or_scratch(
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[restore_checkpoint_cfg], init_rng=jax.random.PRNGKey(0))
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@functools.lru_cache()
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def _get_predict_fn(self, train_state_axes):
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"""Generate a partitioned prediction function for decoding."""
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def partial_predict_fn(params, batch, decode_rng):
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return self.model.predict_batch_with_aux(
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params, batch, decoder_params={'decode_rng': None})
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return self.partitioner.partition(
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partial_predict_fn,
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in_axis_resources=(
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train_state_axes.params,
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t5x.partitioning.PartitionSpec('data',), None),
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out_axis_resources=t5x.partitioning.PartitionSpec('data',)
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)
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def predict_tokens(self, batch, seed=0):
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"""Predict tokens from preprocessed dataset batch."""
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prediction, _ = self._predict_fn(
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self._train_state.params, batch, jax.random.PRNGKey(seed))
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return self.vocabulary.decode_tf(prediction).numpy()
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def __call__(self, audio):
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"""Infer note sequence from audio samples.
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Args:
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audio: 1-d numpy array of audio samples (16kHz) for a single example.
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Returns:
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A note_sequence of the transcribed audio.
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"""
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ds = self.audio_to_dataset(audio)
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ds = self.preprocess(ds)
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model_ds = self.model.FEATURE_CONVERTER_CLS(pack=False)(
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ds, task_feature_lengths=self.sequence_length)
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model_ds = model_ds.batch(self.batch_size)
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inferences = (tokens for batch in model_ds.as_numpy_iterator()
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for tokens in self.predict_tokens(batch))
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predictions = []
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for example, tokens in zip(ds.as_numpy_iterator(), inferences):
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predictions.append(self.postprocess(tokens, example))
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result = metrics_utils.event_predictions_to_ns(
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predictions, codec=self.codec, encoding_spec=self.encoding_spec)
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return result['est_ns']
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def audio_to_dataset(self, audio):
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"""Create a TF Dataset of spectrograms from input audio."""
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frames, frame_times = self._audio_to_frames(audio)
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return tf.data.Dataset.from_tensors({
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'inputs': frames,
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'input_times': frame_times,
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})
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def _audio_to_frames(self, audio):
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"""Compute spectrogram frames from audio."""
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frame_size = self.spectrogram_config.hop_width
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padding = [0, frame_size - len(audio) % frame_size]
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audio = np.pad(audio, padding, mode='constant')
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frames = spectrograms.split_audio(audio, self.spectrogram_config)
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num_frames = len(audio) // frame_size
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times = np.arange(num_frames) / self.spectrogram_config.frames_per_second
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return frames, times
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def preprocess(self, ds):
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pp_chain = [
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functools.partial(
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t5.data.preprocessors.split_tokens_to_inputs_length,
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sequence_length=self.sequence_length,
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output_features=self.output_features,
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feature_key='inputs',
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additional_feature_keys=['input_times']),
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# Cache occurs here during training.
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preprocessors.add_dummy_targets,
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functools.partial(
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preprocessors.compute_spectrograms,
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spectrogram_config=self.spectrogram_config)
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]
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for pp in pp_chain:
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ds = pp(ds)
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return ds
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def postprocess(self, tokens, example):
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tokens = self._trim_eos(tokens)
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start_time = example['input_times'][0]
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# Round down to nearest symbolic token step.
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start_time -= start_time % (1 / self.codec.steps_per_second)
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return {
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'est_tokens': tokens,
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'start_time': start_time,
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# Internal MT3 code expects raw inputs, not used here.
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'raw_inputs': []
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}
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@staticmethod
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def _trim_eos(tokens):
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tokens = np.array(tokens, np.int32)
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if vocabularies.DECODED_EOS_ID in tokens:
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tokens = tokens[:np.argmax(tokens == vocabularies.DECODED_EOS_ID)]
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return tokens
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inference_model = InferenceModel('/home/user/app/checkpoints/mt3/', 'mt3')
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def inference(audio):
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with open(audio, 'rb') as fd:
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contents = fd.read()
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audio = upload_audio(contents,sample_rate=16000)
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est_ns = inference_model(audio)
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note_seq.sequence_proto_to_midi_file(est_ns, './transcribed.mid')
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return './transcribed.mid'
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title = "MT3"
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description = "Gradio demo for MT3: Multi-Task Multitrack Music Transcription. To use it, simply upload your audio file, or click one of the examples to load them. Read more at the links below."
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article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2111.03017' target='_blank'>MT3: Multi-Task Multitrack Music Transcription</a> | <a href='https://github.com/magenta/mt3' target='_blank'>Github Repo</a></p>"
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examples=[['download.wav']]
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gr.Interface(
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inference,
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gr.inputs.Audio(type="filepath", label="Input"),
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[gr.outputs.File(label="Output")],
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title=title,
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description=description,
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article=article,
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examples=examples,
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allow_flagging=False,
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allow_screenshot=False,
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enable_queue=True
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).launch()
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packages.txt
ADDED
@@ -0,0 +1,4 @@
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1 |
+
libfluidsynth1
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2 |
+
build-essential
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3 |
+
libasound2-dev
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4 |
+
libjack-dev
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requirements.txt
ADDED
@@ -0,0 +1,18 @@
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1 |
+
nest-asyncio
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2 |
+
pyfluidsynth
|
3 |
+
absl-py
|
4 |
+
ddsp
|
5 |
+
flax
|
6 |
+
gin-config
|
7 |
+
immutabledict
|
8 |
+
librosa
|
9 |
+
mir_eval
|
10 |
+
note_seq
|
11 |
+
numpy
|
12 |
+
pretty_midi
|
13 |
+
scikit-learn
|
14 |
+
scipy
|
15 |
+
seqio
|
16 |
+
t5
|
17 |
+
tensorflow
|
18 |
+
tensorflow-datasets
|