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Questa collaborazione ti mostrerà come utilizzare il modello SPICE scaricato da TensorFlow Hub.
sudo apt-get install -q -y timidity libsndfile1
Reading package lists... Building dependency tree... Reading state information... The following packages were automatically installed and are no longer required: linux-gcp-5.4-headers-5.4.0-1040 linux-gcp-5.4-headers-5.4.0-1043 linux-gcp-5.4-headers-5.4.0-1044 linux-gcp-5.4-headers-5.4.0-1049 linux-headers-5.4.0-1049-gcp linux-image-5.4.0-1049-gcp 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. Need to get 31.4 MB of archives. After this operation, 40.4 MB of additional disk space will be used. Get:1 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libogg0 amd64 1.3.2-1 [17.2 kB] Get:2 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 freepats all 20060219-1 [29.0 MB] Get:3 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libaudio2 amd64 1.9.4-6 [50.3 kB] Get:4 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libflac8 amd64 1.3.2-1 [213 kB] Get:5 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libsamplerate0 amd64 0.1.9-1 [938 kB] Get:6 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libjack-jackd2-0 amd64 1.9.12~dfsg-2 [263 kB] Get:7 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libvorbis0a amd64 1.3.5-4.2 [86.4 kB] Get:8 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libvorbisenc2 amd64 1.3.5-4.2 [70.7 kB] Get:9 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic-updates/main amd64 libsndfile1 amd64 1.0.28-4ubuntu0.18.04.2 [170 kB] Get:10 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 timidity amd64 2.13.2-41 [585 kB] Get:11 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 timidity-daemon all 2.13.2-41 [5984 B] Fetched 31.4 MB in 2s (14.5 MB/s) Selecting previously unselected package libogg0:amd64. (Reading database ... 281949 files and directories currently installed.) Preparing to unpack .../00-libogg0_1.3.2-1_amd64.deb ... Unpacking libogg0:amd64 (1.3.2-1) ... Selecting previously unselected package freepats. Preparing to unpack .../01-freepats_20060219-1_all.deb ... Unpacking freepats (20060219-1) ... Selecting previously unselected package libaudio2:amd64. Preparing to unpack .../02-libaudio2_1.9.4-6_amd64.deb ... Unpacking libaudio2:amd64 (1.9.4-6) ... Selecting previously unselected package libflac8:amd64. Preparing to unpack .../03-libflac8_1.3.2-1_amd64.deb ... Unpacking libflac8:amd64 (1.3.2-1) ... Selecting previously unselected package libsamplerate0:amd64. Preparing to unpack .../04-libsamplerate0_0.1.9-1_amd64.deb ... Unpacking libsamplerate0:amd64 (0.1.9-1) ... Selecting previously unselected package libjack-jackd2-0:amd64. Preparing to unpack .../05-libjack-jackd2-0_1.9.12~dfsg-2_amd64.deb ... Unpacking libjack-jackd2-0:amd64 (1.9.12~dfsg-2) ... Selecting previously unselected package libvorbis0a:amd64. Preparing to unpack .../06-libvorbis0a_1.3.5-4.2_amd64.deb ... Unpacking libvorbis0a:amd64 (1.3.5-4.2) ... Selecting previously unselected package libvorbisenc2:amd64. Preparing to unpack .../07-libvorbisenc2_1.3.5-4.2_amd64.deb ... Unpacking libvorbisenc2:amd64 (1.3.5-4.2) ... Selecting previously unselected package libsndfile1:amd64. Preparing to unpack .../08-libsndfile1_1.0.28-4ubuntu0.18.04.2_amd64.deb ... Unpacking libsndfile1:amd64 (1.0.28-4ubuntu0.18.04.2) ... Selecting previously unselected package timidity. Preparing to unpack .../09-timidity_2.13.2-41_amd64.deb ... Unpacking timidity (2.13.2-41) ... Selecting previously unselected package timidity-daemon. Preparing to unpack .../10-timidity-daemon_2.13.2-41_all.deb ... Unpacking timidity-daemon (2.13.2-41) ... Setting up libogg0:amd64 (1.3.2-1) ... Setting up libsamplerate0:amd64 (0.1.9-1) ... Setting up freepats (20060219-1) ... Setting up libvorbis0a:amd64 (1.3.5-4.2) ... Setting up libaudio2:amd64 (1.9.4-6) ... Setting up libflac8:amd64 (1.3.2-1) ... Setting up libjack-jackd2-0:amd64 (1.9.12~dfsg-2) ... Setting up libvorbisenc2:amd64 (1.3.5-4.2) ... Setting up timidity (2.13.2-41) ... Setting up libsndfile1:amd64 (1.0.28-4ubuntu0.18.04.2) ... Setting up timidity-daemon (2.13.2-41) ... Adding group timidity....done Adding system user timidity....done Adding user `timidity' to group `audio' ... Adding user timidity to group audio Done. Processing triggers for man-db (2.8.3-2ubuntu0.1) ... Processing triggers for ureadahead (0.100.0-21) ... Processing triggers for libc-bin (2.27-3ubuntu1.2) ... Processing triggers for systemd (237-3ubuntu10.50) ...
# All the imports to deal with sound datapip 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)
Il file di ingresso audio
Ora la parte più difficile: registra il tuo canto! :)
Forniamo quattro metodi per ottenere un file audio:
- Registra l'audio direttamente in colab
- Carica dal tuo computer
- Usa un file salvato su Google Drive
- Scarica il file dal web
Scegli uno dei quattro metodi di seguito.
[Esegui questo] Definizione del codice JS per registrare l'audio direttamente dal browser
RECORD = """
const sleep = time => new Promise(resolve => setTimeout(resolve, time))
const b2text = blob => new Promise(resolve => {
const reader = new FileReader()
reader.onloadend = e => resolve(e.srcElement.result)
reader.readAsDataURL(blob)
})
var record = time => new Promise(async resolve => {
stream = await navigator.mediaDevices.getUserMedia({ audio: true })
recorder = new MediaRecorder(stream)
chunks = []
recorder.ondataavailable = e => chunks.push(e.data)
recorder.start()
await sleep(time)
recorder.onstop = async ()=>{
blob = new Blob(chunks)
text = await b2text(blob)
resolve(text)
}
recorder.stop()
})
"""
def record(sec=5):
try:
from google.colab import output
except ImportError:
print('No possible to import output from google.colab')
return ''
else:
print('Recording')
display(Javascript(RECORD))
s = output.eval_js('record(%d)' % (sec*1000))
fname = 'recorded_audio.wav'
print('Saving to', fname)
b = b64decode(s.split(',')[1])
with open(fname, 'wb') as f:
f.write(b)
return fname
Seleziona come inserire l'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]
Preparazione dei dati audio
Ora abbiamo l'audio, convertiamolo nel formato previsto e poi ascoltiamolo!
Il modello SPICE necessita come input di un file audio con una frequenza di campionamento di 16kHz e con un solo canale (mono).
Per aiutarvi con questa parte, abbiamo creato una funzione ( convert_audio_for_model ) per convertire qualsiasi file wav dovete formato previsto del modello:
# 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
Per prima cosa, diamo un'occhiata alla forma d'onda del nostro canto.
# We can visualize the audio as a waveform.
_ = plt.plot(audio_samples)
Una visualizzazione più informativo è lo spettrogramma che mostra frequenze presenti nel tempo.
Qui usiamo una scala di frequenza logaritmica, per rendere il canto più chiaramente visibile.
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()
Abbiamo bisogno di un'ultima conversione qui. I campioni audio sono in formato int16. Devono essere normalizzati a float tra -1 e 1.
audio_samples = audio_samples / float(MAX_ABS_INT16)
Esecuzione del modello
Ora è la parte facile, cerchiamo di caricare il modello con tensorflow Hub, e si alimentano l'audio ad esso. SPICE ci darà due risultati: passo e incertezza
Tensorflow Hub è una libreria per la pubblicazione, la scoperta, e il consumo di parti riutilizzabili di modelli di apprendimento automatico. Semplifica l'utilizzo dell'apprendimento automatico per risolvere le tue sfide.
Per caricare il modello è sufficiente il modulo Hub e l'URL che punta al modello:
# 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 il modello caricato, i dati preparati, abbiamo bisogno di 3 righe per ottenere il risultato:
# 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()
Semplifichiamo la comprensione dei risultati rimuovendo tutte le stime del passo con bassa confidenza (confidenza < 0,9) e tracciando i restanti.
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()
I valori dell'altezza restituiti da SPICE sono nell'intervallo da 0 a 1. Convertiamoli in valori assoluti dell'altezza in 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 ]
Ora, vediamo quanto è buona la previsione: sovrapporremo le altezze previste allo spettrogramma originale. Per rendere più visibili le previsioni di intonazione, abbiamo cambiato lo spettrogramma in bianco e nero.
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()
Conversione in note musicali
Ora che abbiamo i valori dell'altezza, convertiamoli in note! Questa è una parte che è di per sé una sfida. Dobbiamo tenere in considerazione due cose:
- il resto (quando non si canta)
- la dimensione di ogni nota (offset)
1: aggiunta di zeri all'uscita per indicare quando non si canta
pitch_outputs_and_rests = [
output2hz(p) if c >= 0.9 else 0
for i, p, c in zip(indices, pitch_outputs, confidence_outputs)
]
2: Aggiungere gli offset delle note
Quando una persona canta liberamente, la melodia può avere un offset rispetto ai valori di altezza assoluti che le note possono rappresentare. Quindi, per convertire le previsioni in note, è necessario correggere questo possibile offset. Questo è ciò che calcola il codice seguente.
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
Ora possiamo usare alcune euristiche per cercare di stimare la sequenza più probabile di note che sono state cantate. L'offset ideale calcolato sopra è un ingrediente, ma abbiamo anche bisogno di conoscere la velocità (quante previsioni fanno, diciamo, un ottavo?) e l'offset di tempo per iniziare a quantizzare. Per semplificare, proveremo solo velocità e offset temporali diversi e misureremo l'errore di quantizzazione, utilizzando alla fine i valori che minimizzano questo errore.
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]
Ora scriviamo le note quantizzate come spartiti!
Per farlo useremo due librerie: music21 e aperto Sheet Music di visualizzazione
# 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
[Esegui questo] Funzione di supporto per utilizzare Open Sheet Music Display (codice JS) per mostrare uno spartito musicale
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']
Convertiamo le note musicali in un file MIDI e ascoltiamolo.
Per creare questo file, possiamo usare lo stream che abbiamo creato prima.
# 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
Per ascoltarlo su colab, dobbiamo riconvertirlo in wav. Un modo semplice per farlo è usare 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
E infine, ascolta l'audio, creato dalle note, creato via MIDI dalle altezze previste, dedotte dal modello!
Audio(wav_from_created_midi)