MT3 / app.py
Ahsen Khaliq
Update app.py cd06cc6
import os
os.system("pip install gradio==2.4.6")
import gradio as gr
from pathlib import Path
os.system("pip install gsutil")
os.system("git clone --branch=main https://github.com/google-research/t5x")
os.system("mv t5x t5x_tmp; mv t5x_tmp/* .; rm -r t5x_tmp")
os.system("sed -i 's:jax\[tpu\]:jax:' setup.py")
os.system("python3 -m pip install -e .")
# install mt3
os.system("git clone --branch=main https://github.com/magenta/mt3")
os.system("mv mt3 mt3_tmp; mv mt3_tmp/* .; rm -r mt3_tmp")
os.system("python3 -m pip install -e .")
# copy checkpoints
os.system("gsutil -q -m cp -r gs://mt3/checkpoints .")
# copy soundfont (originally from https://sites.google.com/site/soundfonts4u)
os.system("gsutil -q -m cp gs://magentadata/soundfonts/SGM-v2.01-Sal-Guit-Bass-V1.3.sf2 .")
#@title Imports and Definitions
import functools
import os
import numpy as np
import tensorflow.compat.v2 as tf
import functools
import gin
import jax
import librosa
import note_seq
import seqio
import t5
import t5x
from mt3 import metrics_utils
from mt3 import models
from mt3 import network
from mt3 import note_sequences
from mt3 import preprocessors
from mt3 import spectrograms
from mt3 import vocabularies
import nest_asyncio
nest_asyncio.apply()
SAMPLE_RATE = 16000
SF2_PATH = 'SGM-v2.01-Sal-Guit-Bass-V1.3.sf2'
def upload_audio(audio, sample_rate):
return note_seq.audio_io.wav_data_to_samples_librosa(
audio, sample_rate=sample_rate)
class InferenceModel(object):
"""Wrapper of T5X model for music transcription."""
def __init__(self, checkpoint_path, model_type='mt3'):
# Model Constants.
if model_type == 'ismir2021':
num_velocity_bins = 127
self.encoding_spec = note_sequences.NoteEncodingSpec
self.inputs_length = 512
elif model_type == 'mt3':
num_velocity_bins = 1
self.encoding_spec = note_sequences.NoteEncodingWithTiesSpec
self.inputs_length = 256
else:
raise ValueError('unknown model_type: %s' % model_type)
gin_files = ['/home/user/app/mt3/gin/model.gin',
'/home/user/app/mt3/gin/mt3.gin']
self.batch_size = 8
self.outputs_length = 1024
self.sequence_length = {'inputs': self.inputs_length,
'targets': self.outputs_length}
self.partitioner = t5x.partitioning.ModelBasedPjitPartitioner(
model_parallel_submesh=(1, 1, 1, 1), num_partitions=1)
# Build Codecs and Vocabularies.
self.spectrogram_config = spectrograms.SpectrogramConfig()
self.codec = vocabularies.build_codec(
vocab_config=vocabularies.VocabularyConfig(
num_velocity_bins=num_velocity_bins))
self.vocabulary = vocabularies.vocabulary_from_codec(self.codec)
self.output_features = {
'inputs': seqio.ContinuousFeature(dtype=tf.float32, rank=2),
'targets': seqio.Feature(vocabulary=self.vocabulary),
}
# Create a T5X model.
self._parse_gin(gin_files)
self.model = self._load_model()
# Restore from checkpoint.
self.restore_from_checkpoint(checkpoint_path)
@property
def input_shapes(self):
return {
'encoder_input_tokens': (self.batch_size, self.inputs_length),
'decoder_input_tokens': (self.batch_size, self.outputs_length)
}
def _parse_gin(self, gin_files):
"""Parse gin files used to train the model."""
gin_bindings = [
'from __gin__ import dynamic_registration',
'from mt3 import vocabularies',
'VOCAB_CONFIG=@vocabularies.VocabularyConfig()',
'vocabularies.VocabularyConfig.num_velocity_bins=%NUM_VELOCITY_BINS'
]
with gin.unlock_config():
gin.parse_config_files_and_bindings(
gin_files, gin_bindings, finalize_config=False)
def _load_model(self):
"""Load up a T5X `Model` after parsing training gin config."""
model_config = gin.get_configurable(network.T5Config)()
module = network.Transformer(config=model_config)
return models.ContinuousInputsEncoderDecoderModel(
module=module,
input_vocabulary=self.output_features['inputs'].vocabulary,
output_vocabulary=self.output_features['targets'].vocabulary,
optimizer_def=t5x.adafactor.Adafactor(decay_rate=0.8, step_offset=0),
input_depth=spectrograms.input_depth(self.spectrogram_config))
def restore_from_checkpoint(self, checkpoint_path):
"""Restore training state from checkpoint, resets self._predict_fn()."""
train_state_initializer = t5x.utils.TrainStateInitializer(
optimizer_def=self.model.optimizer_def,
init_fn=self.model.get_initial_variables,
input_shapes=self.input_shapes,
partitioner=self.partitioner)
restore_checkpoint_cfg = t5x.utils.RestoreCheckpointConfig(
path=checkpoint_path, mode='specific', dtype='float32')
train_state_axes = train_state_initializer.train_state_axes
self._predict_fn = self._get_predict_fn(train_state_axes)
self._train_state = train_state_initializer.from_checkpoint_or_scratch(
[restore_checkpoint_cfg], init_rng=jax.random.PRNGKey(0))
@functools.lru_cache()
def _get_predict_fn(self, train_state_axes):
"""Generate a partitioned prediction function for decoding."""
def partial_predict_fn(params, batch, decode_rng):
return self.model.predict_batch_with_aux(
params, batch, decoder_params={'decode_rng': None})
return self.partitioner.partition(
partial_predict_fn,
in_axis_resources=(
train_state_axes.params,
t5x.partitioning.PartitionSpec('data',), None),
out_axis_resources=t5x.partitioning.PartitionSpec('data',)
)
def predict_tokens(self, batch, seed=0):
"""Predict tokens from preprocessed dataset batch."""
prediction, _ = self._predict_fn(
self._train_state.params, batch, jax.random.PRNGKey(seed))
return self.vocabulary.decode_tf(prediction).numpy()
def __call__(self, audio):
"""Infer note sequence from audio samples.
Args:
audio: 1-d numpy array of audio samples (16kHz) for a single example.
Returns:
A note_sequence of the transcribed audio.
"""
ds = self.audio_to_dataset(audio)
ds = self.preprocess(ds)
model_ds = self.model.FEATURE_CONVERTER_CLS(pack=False)(
ds, task_feature_lengths=self.sequence_length)
model_ds = model_ds.batch(self.batch_size)
inferences = (tokens for batch in model_ds.as_numpy_iterator()
for tokens in self.predict_tokens(batch))
predictions = []
for example, tokens in zip(ds.as_numpy_iterator(), inferences):
predictions.append(self.postprocess(tokens, example))
result = metrics_utils.event_predictions_to_ns(
predictions, codec=self.codec, encoding_spec=self.encoding_spec)
return result['est_ns']
def audio_to_dataset(self, audio):
"""Create a TF Dataset of spectrograms from input audio."""
frames, frame_times = self._audio_to_frames(audio)
return tf.data.Dataset.from_tensors({
'inputs': frames,
'input_times': frame_times,
})
def _audio_to_frames(self, audio):
"""Compute spectrogram frames from audio."""
frame_size = self.spectrogram_config.hop_width
padding = [0, frame_size - len(audio) % frame_size]
audio = np.pad(audio, padding, mode='constant')
frames = spectrograms.split_audio(audio, self.spectrogram_config)
num_frames = len(audio) // frame_size
times = np.arange(num_frames) / self.spectrogram_config.frames_per_second
return frames, times
def preprocess(self, ds):
pp_chain = [
functools.partial(
t5.data.preprocessors.split_tokens_to_inputs_length,
sequence_length=self.sequence_length,
output_features=self.output_features,
feature_key='inputs',
additional_feature_keys=['input_times']),
# Cache occurs here during training.
preprocessors.add_dummy_targets,
functools.partial(
preprocessors.compute_spectrograms,
spectrogram_config=self.spectrogram_config)
]
for pp in pp_chain:
ds = pp(ds)
return ds
def postprocess(self, tokens, example):
tokens = self._trim_eos(tokens)
start_time = example['input_times'][0]
# Round down to nearest symbolic token step.
start_time -= start_time % (1 / self.codec.steps_per_second)
return {
'est_tokens': tokens,
'start_time': start_time,
# Internal MT3 code expects raw inputs, not used here.
'raw_inputs': []
}
@staticmethod
def _trim_eos(tokens):
tokens = np.array(tokens, np.int32)
if vocabularies.DECODED_EOS_ID in tokens:
tokens = tokens[:np.argmax(tokens == vocabularies.DECODED_EOS_ID)]
return tokens
inference_model = InferenceModel('/home/user/app/checkpoints/mt3/', 'mt3')
def inference(audio):
with open(audio, 'rb') as fd:
contents = fd.read()
audio = upload_audio(contents,sample_rate=16000)
est_ns = inference_model(audio)
note_seq.sequence_proto_to_midi_file(est_ns, './transcribed.mid')
return './transcribed.mid'
title = "MT3"
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."
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>"
examples=[['download.wav']]
gr.Interface(
inference,
gr.inputs.Audio(type="filepath", label="Input"),
[gr.outputs.File(label="Output")],
title=title,
description=description,
article=article,
examples=examples,
allow_flagging=False,
allow_screenshot=False,
enable_queue=True
).launch()