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Detección de tono con SPICE

Ver en TensorFlow.org Ejecutar en Google Colab Ver en GitHub Descargar cuaderno Ver modelo TF Hub

Este colab le mostrará cómo usar el modelo SPICE descargado de TensorFlow Hub.

sudo apt-get install -q -y timidity libsndfile1
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  linux-gcp-5.4-headers-5.4.0-1040 linux-gcp-5.4-headers-5.4.0-1043
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  linux-modules-5.4.0-1049-gcp linux-modules-extra-5.4.0-1049-gcp
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
  freepats libaudio2 libflac8 libjack-jackd2-0 libogg0 libsamplerate0
  libvorbis0a libvorbisenc2 timidity-daemon
Suggested packages:
  nas jackd2 fluid-soundfont-gm fluid-soundfont-gs pmidi
The following NEW packages will be installed:
  freepats libaudio2 libflac8 libjack-jackd2-0 libogg0 libsamplerate0
  libsndfile1 libvorbis0a libvorbisenc2 timidity timidity-daemon
0 upgraded, 11 newly installed, 0 to remove and 143 not upgraded.
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# All the imports to deal with sound data
pip install pydub numba==0.48 librosa music21
import tensorflow as tf
import tensorflow_hub as hub

import numpy as np
import matplotlib.pyplot as plt
import librosa
from librosa import display as librosadisplay

import logging
import math
import statistics
import sys

from IPython.display import Audio, Javascript
from scipy.io import wavfile

from base64 import b64decode

import music21
from pydub import AudioSegment

logger = logging.getLogger()
logger.setLevel(logging.ERROR)

print("tensorflow: %s" % tf.__version__)
#print("librosa: %s" % librosa.__version__)
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/numba/errors.py:137: UserWarning: Insufficiently recent colorama version found. Numba requires colorama >= 0.3.9
  warnings.warn(msg)
tensorflow: 2.7.0
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/pydub/utils.py:170: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work
  warn("Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work", RuntimeWarning)

El archivo de entrada de audio

Ahora la parte más difícil: ¡graba tu canto! :)

Ofrecemos cuatro métodos para obtener un archivo de audio:

  1. Grabe audio directamente en colab
  2. Sube desde tu computadora
  3. Utilice un archivo guardado en Google Drive
  4. Descarga el archivo de la web

Elija uno de los cuatro métodos siguientes.

[Ejecutar esto] Definición del código JS para grabar audio directamente desde el navegador

Seleccione cómo ingresar su audio

INPUT_SOURCE = 'https://storage.googleapis.com/download.tensorflow.org/data/c-scale-metronome.wav'

print('You selected', INPUT_SOURCE)

if INPUT_SOURCE == 'RECORD':
  uploaded_file_name = record(5)
elif INPUT_SOURCE == 'UPLOAD':
  try:
    from google.colab import files
  except ImportError:
    print("ImportError: files from google.colab seems to not be available")
  else:
    uploaded = files.upload()
    for fn in uploaded.keys():
      print('User uploaded file "{name}" with length {length} bytes'.format(
          name=fn, length=len(uploaded[fn])))
    uploaded_file_name = next(iter(uploaded))
    print('Uploaded file: ' + uploaded_file_name)
elif INPUT_SOURCE.startswith('./drive/'):
  try:
    from google.colab import drive
  except ImportError:
    print("ImportError: files from google.colab seems to not be available")
  else:
    drive.mount('/content/drive')
    # don't forget to change the name of the file you
    # will you here!
    gdrive_audio_file = 'YOUR_MUSIC_FILE.wav'
    uploaded_file_name = INPUT_SOURCE
elif INPUT_SOURCE.startswith('http'):
  !wget --no-check-certificate 'https://storage.googleapis.com/download.tensorflow.org/data/c-scale-metronome.wav' -O c-scale.wav
  uploaded_file_name = 'c-scale.wav'
else:
  print('Unrecognized input format!')
  print('Please select "RECORD", "UPLOAD", or specify a file hosted on Google Drive or a file from the web to download file to download')
You selected https://storage.googleapis.com/download.tensorflow.org/data/c-scale-metronome.wav
--2021-11-05 11:10:55--  https://storage.googleapis.com/download.tensorflow.org/data/c-scale-metronome.wav
Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.97.128, 64.233.189.128, 74.125.203.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.97.128|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 384728 (376K) [audio/wav]
Saving to: ‘c-scale.wav’

c-scale.wav         100%[===================>] 375.71K  --.-KB/s    in 0.006s  

2021-11-05 11:10:56 (65.4 MB/s) - ‘c-scale.wav’ saved [384728/384728]

Preparando los datos de audio

Ahora que tenemos el audio, ¡conviértalo al formato esperado y luego escúchelo!

El modelo SPICE necesita como entrada un archivo de audio a una frecuencia de muestreo de 16kHz y con un solo canal (mono).

Para ayudarle con esta parte, hemos creado una función ( convert_audio_for_model ) para convertir cualquier archivo de sonido que tiene que formato esperado del modelo:

# Function that converts the user-created audio to the format that the model 
# expects: bitrate 16kHz and only one channel (mono).

EXPECTED_SAMPLE_RATE = 16000

def convert_audio_for_model(user_file, output_file='converted_audio_file.wav'):
  audio = AudioSegment.from_file(user_file)
  audio = audio.set_frame_rate(EXPECTED_SAMPLE_RATE).set_channels(1)
  audio.export(output_file, format="wav")
  return output_file
# Converting to the expected format for the model
# in all the input 4 input method before, the uploaded file name is at
# the variable uploaded_file_name
converted_audio_file = convert_audio_for_model(uploaded_file_name)
# Loading audio samples from the wav file:
sample_rate, audio_samples = wavfile.read(converted_audio_file, 'rb')

# Show some basic information about the audio.
duration = len(audio_samples)/sample_rate
print(f'Sample rate: {sample_rate} Hz')
print(f'Total duration: {duration:.2f}s')
print(f'Size of the input: {len(audio_samples)}')

# Let's listen to the wav file.
Audio(audio_samples, rate=sample_rate)
Sample rate: 16000 Hz
Total duration: 11.89s
Size of the input: 190316

Primero, echemos un vistazo a la forma de onda de nuestro canto.

# We can visualize the audio as a waveform.
_ = plt.plot(audio_samples)

png

Una visualización más informativo es el espectrograma , que muestra frecuencias presentes en el tiempo.

Aquí, usamos una escala de frecuencia logarítmica, para hacer que el canto sea más claramente visible.

MAX_ABS_INT16 = 32768.0

def plot_stft(x, sample_rate, show_black_and_white=False):
  x_stft = np.abs(librosa.stft(x, n_fft=2048))
  fig, ax = plt.subplots()
  fig.set_size_inches(20, 10)
  x_stft_db = librosa.amplitude_to_db(x_stft, ref=np.max)
  if(show_black_and_white):
    librosadisplay.specshow(data=x_stft_db, y_axis='log', 
                             sr=sample_rate, cmap='gray_r')
  else:
    librosadisplay.specshow(data=x_stft_db, y_axis='log', sr=sample_rate)

  plt.colorbar(format='%+2.0f dB')

plot_stft(audio_samples / MAX_ABS_INT16 , sample_rate=EXPECTED_SAMPLE_RATE)
plt.show()

png

Necesitamos una última conversión aquí. Las muestras de audio están en formato int16. Deben normalizarse a flotadores entre -1 y 1.

audio_samples = audio_samples / float(MAX_ABS_INT16)

Ejecutando el modelo

Ahora es la parte fácil, vamos a cargar el modelo con TensorFlow Hub, y se alimentan el audio a la misma. SPICE nos dará dos salidas: tono e incertidumbre

TensorFlow Hub es una biblioteca para la publicación, descubrimiento, y el consumo de partes reutilizables de modelos de aprendizaje automático. Hace que el aprendizaje automático sea fácil de usar para resolver sus desafíos.

Para cargar el modelo solo necesita el módulo Hub y la URL que apunta al modelo:

# Loading the SPICE model is easy:
model = hub.load("https://tfhub.dev/google/spice/2")
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/conv2d/kernel:0' shape=(1, 3, 1, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/conv2d/kernel:0' shape=(1, 3, 1, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/conv2d/kernel:0' shape=(1, 3, 1, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/conv2d/kernel:0' shape=(1, 3, 1, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/conv2d/kernel:0' shape=(1, 3, 1, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/conv2d/kernel:0' shape=(1, 3, 1, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'global_step:0' shape=() dtype=int64_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/conv2d/kernel:0' shape=(1, 3, 1, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/conv2d/kernel:0' shape=(1, 3, 1, 64) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/gamma:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/beta:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().
WARNING:tensorflow:Unable to create a python object for variable <tf.Variable 'encoder/batch_normalization/moving_mean:0' shape=(64,) dtype=float32_ref> because it is a reference variable. It may not be visible to training APIs. If this is a problem, consider rebuilding the SavedModel after running tf.compat.v1.enable_resource_variables().

Con el modelo cargado, los datos preparados, necesitamos 3 líneas para obtener el resultado:

# We now feed the audio to the SPICE tf.hub model to obtain pitch and uncertainty outputs as tensors.
model_output = model.signatures["serving_default"](tf.constant(audio_samples, tf.float32))

pitch_outputs = model_output["pitch"]
uncertainty_outputs = model_output["uncertainty"]

# 'Uncertainty' basically means the inverse of confidence.
confidence_outputs = 1.0 - uncertainty_outputs

fig, ax = plt.subplots()
fig.set_size_inches(20, 10)
plt.plot(pitch_outputs, label='pitch')
plt.plot(confidence_outputs, label='confidence')
plt.legend(loc="lower right")
plt.show()

png

Hagamos que los resultados sean más fáciles de entender eliminando todas las estimaciones de tono con poca confianza (confianza <0,9) y graficando las restantes.

confidence_outputs = list(confidence_outputs)
pitch_outputs = [ float(x) for x in pitch_outputs]

indices = range(len (pitch_outputs))
confident_pitch_outputs = [ (i,p)  
  for i, p, c in zip(indices, pitch_outputs, confidence_outputs) if  c >= 0.9  ]
confident_pitch_outputs_x, confident_pitch_outputs_y = zip(*confident_pitch_outputs)

fig, ax = plt.subplots()
fig.set_size_inches(20, 10)
ax.set_ylim([0, 1])
plt.scatter(confident_pitch_outputs_x, confident_pitch_outputs_y, )
plt.scatter(confident_pitch_outputs_x, confident_pitch_outputs_y, c="r")

plt.show()

png

Los valores de tono devueltos por SPICE están en el rango de 0 a 1. Vamos a convertirlos en valores de tono absoluto en Hz.

def output2hz(pitch_output):
  # Constants taken from https://tfhub.dev/google/spice/2
  PT_OFFSET = 25.58
  PT_SLOPE = 63.07
  FMIN = 10.0;
  BINS_PER_OCTAVE = 12.0;
  cqt_bin = pitch_output * PT_SLOPE + PT_OFFSET;
  return FMIN * 2.0 ** (1.0 * cqt_bin / BINS_PER_OCTAVE)

confident_pitch_values_hz = [ output2hz(p) for p in confident_pitch_outputs_y ]

Ahora, veamos qué tan buena es la predicción: superpondremos los tonos predichos sobre el espectrograma original. Para hacer más visibles las predicciones de tono, cambiamos el espectrograma a blanco y negro.

plot_stft(audio_samples / MAX_ABS_INT16 , 
          sample_rate=EXPECTED_SAMPLE_RATE, show_black_and_white=True)
# Note: conveniently, since the plot is in log scale, the pitch outputs 
# also get converted to the log scale automatically by matplotlib.
plt.scatter(confident_pitch_outputs_x, confident_pitch_values_hz, c="r")

plt.show()

png

Conversión a notas musicales

Ahora que tenemos los valores de tono, ¡conviértelos en notas! Esta parte es un desafío en sí misma. Tenemos que tener en cuenta dos cosas:

  1. los descansos (cuando no hay canto)
  2. el tamaño de cada nota (compensaciones)

1: Añadiendo ceros a la salida para indicar cuando no hay canto

pitch_outputs_and_rests = [
    output2hz(p) if c >= 0.9 else 0
    for i, p, c in zip(indices, pitch_outputs, confidence_outputs)
]

2: Agregar compensaciones de notas

Cuando una persona canta libremente, la melodía puede tener un desplazamiento de los valores absolutos de tono que pueden representar las notas. Por lo tanto, para convertir las predicciones en notas, es necesario corregir este posible desplazamiento. Esto es lo que calcula el siguiente código.

A4 = 440
C0 = A4 * pow(2, -4.75)
note_names = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"]

def hz2offset(freq):
  # This measures the quantization error for a single note.
  if freq == 0:  # Rests always have zero error.
    return None
  # Quantized note.
  h = round(12 * math.log2(freq / C0))
  return 12 * math.log2(freq / C0) - h


# The ideal offset is the mean quantization error for all the notes
# (excluding rests):
offsets = [hz2offset(p) for p in pitch_outputs_and_rests if p != 0]
print("offsets: ", offsets)

ideal_offset = statistics.mean(offsets)
print("ideal offset: ", ideal_offset)
offsets:  [0.2851075707500712, 0.3700368844097355, 0.2861639241998972, 0.19609005646164235, 0.17851737247163868, 0.27334483073408933, -0.4475316266590852, -0.24651997073237908, -0.1796558047706398, -0.23060136331860548, -0.3782634107643901, -0.4725100625926686, -0.3457194541269999, -0.2436666886383776, -0.1818906877810207, -0.1348077739650435, -0.24551812662426897, -0.4454903457934165, -0.3126792745167535, -0.12241723670307181, -0.06614479972665066, -0.06702634735648871, -0.1744135098034576, -0.29365551425759406, -0.32520890458170726, -0.056438377636119696, 0.1470525135224534, 0.17167006002122775, 0.16529246704037348, 0.09569531546290477, -0.006323616641203955, -0.11799822075907684, -0.18835098459069144, -0.17934754504506145, -0.17215419157092526, -0.23695828034226452, -0.34594501002376177, -0.39380045278613807, -0.2528674895936689, -0.11009248657768467, -0.07118597401920113, -0.08042248799149121, -0.12799598588293293, -0.16227484329287023, -0.05931985421721464, 0.10667800800259641, 0.21044687793906292, 0.2931939382975841, -0.22329278631751492, -0.12365553720538003, -0.4571117360765271, -0.34864566459005175, -0.35947798653189267, -0.4313175396496476, -0.4818928106004421, 0.44220950977261, 0.45883109973128455, -0.47095522924010425, -0.3674495078498552, -0.3047186536962201, -0.31075979246441676, -0.4501382996017185, 0.3966096259778311, 0.4238116671269694, 0.4982676686471237, -0.45932030423227843, -0.4890504510576079, 0.3836871527260044, 0.4441304941600137, -0.38787359430138935, -0.24855899466817277, -0.20666386647764057, -0.23811575664822726, -0.2760223047310504, -0.3641714288169524, -0.41670903606955534, -0.41009272976462086, -0.3340427999073796, -0.26122959716860805, -0.2232610212141708, -0.19940660549943345, -0.22528914465252825, -0.2780899004513415, -0.2744434134537457, -0.25654931231085953, -0.33068201704567457, -0.4678933079416083, -0.4695135511333177, -0.1648153518015647, -0.24618840082233362, -0.48052406086269883, -0.3771743489677135, -0.32261801643912236, -0.25560347987954657, -0.24629741950576545, -0.14035005553309787, -0.16659160448853783, -0.2442749349648139, -0.236978201704666, -0.20882506652418442, -0.22637331529204374, -0.29836135937516417, -0.39081484182421633, -0.3909877680117404, -0.3650093676025108, -0.2642347521955202, -0.13023199393098395, -0.18214744283501716, -0.3020867909366345, -0.33754229827467697, -0.34391801162306024, -0.31454499496763333, -0.26713502510135356, -0.2910439501578139, -0.11686573876684037, -0.1673094354445226, -0.24345334692542053, -0.30852998240535356, -0.35647376789395935, -0.37154654069487236, -0.3600149954730796, -0.2667062802488047, -0.21902000440899627, -0.2484456507736752, -0.2774107871825038, -0.2941432754570741, -0.31118778272216474, -0.32662896348779213, -0.3053947554403962, -0.2160201109821145, -0.17343703730647775, -0.17792559965198507, -0.19880643679444177, -0.2725068260604502, -0.3152120758468442, -0.28217377586905457, -0.11595223738495974, 0.0541902144377957, 0.11488166735824024, -0.2559698195630773, 0.01930235610660702, -0.002236352401425279, 0.4468796487277231, 0.15514959977323883, 0.4207694853966899, 0.3854474319642236, 0.4373497234409598, -0.4694994504625001, -0.3662719146782649, -0.20354085369650932, -0.015043790774988963, -0.4185651697093675, -0.17896653874461066, -0.032896162706066434, -0.061098168330843805, -0.1953772325689087, -0.2545198683315988, -0.3363741032654488, -0.39191536320988973, -0.36531668408458984, -0.3489657612020167, -0.35455202891175475, -0.38925192399566555, 0.48781635300571935, -0.2820884378129733, -0.241939488189864, -0.24987341685836384, -0.3034880535179809, -0.2910712014014081, -0.2783103765422581, -0.30017802073304267, -0.23735882385318519, -0.15802705569807785, -0.1688725350672513, 0.00533368216211727, -0.2545762573057857, -0.28210347487274845, -0.29791870250051034, -0.3228369901949648, -0.3895802937323367, 0.4323827980583488, 0.17439196334535723, -0.12961039467398905, -0.2236296109730489, -0.04022635205333813, -0.4264043621594098, -0.0019025255615048309, -0.07466309859101727, -0.08665327413623203, -0.08169104440753472, -0.31617519541327965, -0.47420548422877573, 0.1502044753855003, 0.30507923857624064, 0.031032583278971515, -0.17852388186996393, -0.3371347884709195, -0.41780861421172233, -0.2023933346444835, -0.10604901297633518, -0.10771248771493447, -0.16037790997569346, -0.18698410763089868, -0.17355977250879562, -0.008242337244190878, -0.011401999431292609, -0.1876701734835322, -0.3601715640598968, 0.011681766969516616, -0.1931417836124183]
ideal offset:  -0.16889341450193418

Ahora podemos usar algunas heurísticas para intentar estimar la secuencia más probable de notas cantadas. La compensación ideal calculada anteriormente es un ingrediente, pero también necesitamos saber la velocidad (¿cuántas predicciones hacen, digamos, un octavo?) Y la compensación de tiempo para comenzar a cuantificar. Para simplificar, simplemente probaremos diferentes velocidades y compensaciones de tiempo y mediremos el error de cuantificación, utilizando al final los valores que minimicen este error.

def quantize_predictions(group, ideal_offset):
  # Group values are either 0, or a pitch in Hz.
  non_zero_values = [v for v in group if v != 0]
  zero_values_count = len(group) - len(non_zero_values)

  # Create a rest if 80% is silent, otherwise create a note.
  if zero_values_count > 0.8 * len(group):
    # Interpret as a rest. Count each dropped note as an error, weighted a bit
    # worse than a badly sung note (which would 'cost' 0.5).
    return 0.51 * len(non_zero_values), "Rest"
  else:
    # Interpret as note, estimating as mean of non-rest predictions.
    h = round(
        statistics.mean([
            12 * math.log2(freq / C0) - ideal_offset for freq in non_zero_values
        ]))
    octave = h // 12
    n = h % 12
    note = note_names[n] + str(octave)
    # Quantization error is the total difference from the quantized note.
    error = sum([
        abs(12 * math.log2(freq / C0) - ideal_offset - h)
        for freq in non_zero_values
    ])
    return error, note


def get_quantization_and_error(pitch_outputs_and_rests, predictions_per_eighth,
                               prediction_start_offset, ideal_offset):
  # Apply the start offset - we can just add the offset as rests.
  pitch_outputs_and_rests = [0] * prediction_start_offset + \
                            pitch_outputs_and_rests
  # Collect the predictions for each note (or rest).
  groups = [
      pitch_outputs_and_rests[i:i + predictions_per_eighth]
      for i in range(0, len(pitch_outputs_and_rests), predictions_per_eighth)
  ]

  quantization_error = 0

  notes_and_rests = []
  for group in groups:
    error, note_or_rest = quantize_predictions(group, ideal_offset)
    quantization_error += error
    notes_and_rests.append(note_or_rest)

  return quantization_error, notes_and_rests


best_error = float("inf")
best_notes_and_rests = None
best_predictions_per_note = None

for predictions_per_note in range(20, 65, 1):
  for prediction_start_offset in range(predictions_per_note):

    error, notes_and_rests = get_quantization_and_error(
        pitch_outputs_and_rests, predictions_per_note,
        prediction_start_offset, ideal_offset)

    if error < best_error:      
      best_error = error
      best_notes_and_rests = notes_and_rests
      best_predictions_per_note = predictions_per_note

# At this point, best_notes_and_rests contains the best quantization.
# Since we don't need to have rests at the beginning, let's remove these:
while best_notes_and_rests[0] == 'Rest':
  best_notes_and_rests = best_notes_and_rests[1:]
# Also remove silence at the end.
while best_notes_and_rests[-1] == 'Rest':
  best_notes_and_rests = best_notes_and_rests[:-1]

¡Ahora escribamos las notas cuantificadas como partitura!

Para hacerlo utilizaremos dos bibliotecas: music21 y Hoja de Open Music Display

# Creating the sheet music score.
sc = music21.stream.Score()
# Adjust the speed to match the actual singing.
bpm = 60 * 60 / best_predictions_per_note
print ('bpm: ', bpm)
a = music21.tempo.MetronomeMark(number=bpm)
sc.insert(0,a)

for snote in best_notes_and_rests:   
    d = 'half'
    if snote == 'Rest':      
      sc.append(music21.note.Rest(type=d))
    else:
      sc.append(music21.note.Note(snote, type=d))
bpm:  78.26086956521739

[Ejecutar esto] Función de ayuda para usar Open Sheet Music Display (código JS) para mostrar una partitura musical

from IPython.core.display import display, HTML, Javascript
import json, random

def showScore(score):
    xml = open(score.write('musicxml')).read()
    showMusicXML(xml)

def showMusicXML(xml):
    DIV_ID = "OSMD_div"
    display(HTML('<div id="'+DIV_ID+'">loading OpenSheetMusicDisplay</div>'))
    script = """
    var div_id = { {DIV_ID} };
    function loadOSMD() { 
        return new Promise(function(resolve, reject){
            if (window.opensheetmusicdisplay) {
                return resolve(window.opensheetmusicdisplay)
            }
            // OSMD script has a 'define' call which conflicts with requirejs
            var _define = window.define // save the define object 
            window.define = undefined // now the loaded script will ignore requirejs
            var s = document.createElement( 'script' );
            s.setAttribute( 'src', "https://cdn.jsdelivr.net/npm/opensheetmusicdisplay@0.7.6/build/opensheetmusicdisplay.min.js" );
            //s.setAttribute( 'src', "/custom/opensheetmusicdisplay.js" );
            s.onload=function(){
                window.define = _define
                resolve(opensheetmusicdisplay);
            };
            document.body.appendChild( s ); // browser will try to load the new script tag
        }) 
    }
    loadOSMD().then((OSMD)=>{
        window.openSheetMusicDisplay = new OSMD.OpenSheetMusicDisplay(div_id, {
          drawingParameters: "compacttight"
        });
        openSheetMusicDisplay
            .load({ {data} })
            .then(
              function() {
                openSheetMusicDisplay.render();
              }
            );
    })
    """.replace('{ {DIV_ID} }',DIV_ID).replace('{ {data} }',json.dumps(xml))
    display(Javascript(script))
    return
# rendering the music score
showScore(sc)
print(best_notes_and_rests)
/tmpfs/src/tf_docs_env/lib/python3.7/site-packages/music21/musicxml/m21ToXml.py:465: MusicXMLWarning: <music21.stream.Score 0x7f276c652190> is not well-formed; see isWellFormedNotation()
  category=MusicXMLWarning)
<IPython.core.display.Javascript object>
['C3', 'D3', 'E3', 'F3', 'G3', 'A3', 'B3', 'C4']

Convirtamos las notas musicales a un archivo MIDI y escuchémoslo.

Para crear este archivo, podemos usar la secuencia que creamos antes.

# Saving the recognized musical notes as a MIDI file
converted_audio_file_as_midi = converted_audio_file[:-4] + '.mid'
fp = sc.write('midi', fp=converted_audio_file_as_midi)
wav_from_created_midi = converted_audio_file_as_midi.replace(' ', '_') + "_midioutput.wav"
print(wav_from_created_midi)
converted_audio_file.mid_midioutput.wav

Para escucharlo en colab, necesitamos convertirlo de nuevo a wav. Una forma sencilla de hacerlo es utilizar Timidity.

timidity $converted_audio_file_as_midi -Ow -o $wav_from_created_midi
Playing converted_audio_file.mid
MIDI file: converted_audio_file.mid
Format: 1  Tracks: 2  Divisions: 1024
Track name: 
Playing time: ~16 seconds
Notes cut: 0
Notes lost totally: 0

¡Y finalmente, escuche el audio, creado a partir de notas, creado a través de MIDI a partir de los tonos predichos, inferidos por el modelo!

Audio(wav_from_created_midi)