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स्पाइस के साथ पिच का पता लगाना

TensorFlow.org पर देखें Google Colab में चलाएं GitHub पर देखें नोटबुक डाउनलोड करें TF हब मॉडल देखें

यह कोलाब आपको दिखाएगा कि TensorFlow Hub से डाउनलोड किए गए SPICE मॉडल का उपयोग कैसे करें।

sudo apt-get install -q -y timidity libsndfile1
Reading package lists...
Building dependency tree...
Reading state information...
libsndfile1 is already the newest version (1.0.28-4ubuntu0.18.04.1).
libsndfile1 set to manually installed.
The following packages were automatically installed and are no longer required:
  dconf-gsettings-backend dconf-service dkms freeglut3 freeglut3-dev
  glib-networking glib-networking-common glib-networking-services
  gsettings-desktop-schemas libcairo-gobject2 libcolord2 libdconf1
  libegl1-mesa libepoxy0 libglu1-mesa libglu1-mesa-dev libgtk-3-0
  libgtk-3-common libice-dev libjansson4 libjson-glib-1.0-0
  libjson-glib-1.0-common libproxy1v5 librest-0.7-0 libsm-dev
  libsoup-gnome2.4-1 libsoup2.4-1 libwayland-cursor0 libwayland-egl1 libxfont2
  libxi-dev libxkbcommon0 libxkbfile1 libxmu-dev libxmu-headers libxnvctrl0
  libxt-dev linux-gcp-headers-5.0.0-1026 linux-headers-5.0.0-1026-gcp
  linux-image-5.0.0-1026-gcp linux-modules-5.0.0-1026-gcp pkg-config
  policykit-1-gnome python3-xkit screen-resolution-extra x11-xkb-utils
  xserver-common xserver-xorg-core-hwe-18.04
Use 'sudo apt autoremove' to remove them.
The following additional packages will be installed:
  freepats libaudio2 libjack-jackd2-0 libsamplerate0 timidity-daemon
Suggested packages:
  nas jackd2 fluid-soundfont-gm fluid-soundfont-gs pmidi
The following NEW packages will be installed:
  freepats libaudio2 libjack-jackd2-0 libsamplerate0 timidity timidity-daemon
0 upgraded, 6 newly installed, 0 to remove and 102 not upgraded.
Need to get 30.8 MB of archives.
After this operation, 38.3 MB of additional disk space will be used.
Get:1 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 freepats all 20060219-1 [29.0 MB]
Get:2 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libaudio2 amd64 1.9.4-6 [50.3 kB]
Get:3 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libsamplerate0 amd64 0.1.9-1 [938 kB]
Get:4 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/main amd64 libjack-jackd2-0 amd64 1.9.12~dfsg-2 [263 kB]
Get:5 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 timidity amd64 2.13.2-41 [585 kB]
Get:6 http://asia-east1.gce.archive.ubuntu.com/ubuntu bionic/universe amd64 timidity-daemon all 2.13.2-41 [5984 B]
Fetched 30.8 MB in 3s (9707 kB/s)
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Setting up libjack-jackd2-0:amd64 (1.9.12~dfsg-2) ...
Setting up timidity (2.13.2-41) ...
Setting up timidity-daemon (2.13.2-41) ...
Adding group timidity....done
Adding system user timidity....done
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Adding user timidity to group audio
Done.
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# All the imports to deal with sound data
pip install -q 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__)
03094099990

ऑडियो इनपुट फ़ाइल

अब सबसे कठिन हिस्सा: अपने गायन को रिकॉर्ड करें! :)

हम ऑडियो फ़ाइल प्राप्त करने के लिए चार तरीके प्रदान करते हैं:

  1. सीधे कोलाब में रिकॉर्ड ऑडियो
  2. अपने कंप्यूटर से अपलोड करें
  3. Google डिस्क पर सहेजी गई फ़ाइल का उपयोग करें
  4. वेब से फ़ाइल डाउनलोड करें

नीचे दिए गए चार तरीकों में से एक चुनें।

[इसे चलाएं] जेएस कोड की परिभाषा ब्राउज़र से सीधे ऑडियो रिकॉर्ड करने के लिए

अपने ऑडियो को इनपुट करने का तरीका चुनें

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
--2020-11-24 16:33:19--  https://storage.googleapis.com/download.tensorflow.org/data/c-scale-metronome.wav
Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.125.128, 74.125.204.128, 64.233.189.128, ...
Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.125.128|: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 0.004s  

2020-11-24 16:33:20 (103 MB/s) - ‘c-scale.wav’ saved [384728/384728]


ऑडियो डेटा तैयार करना

अब हमारे पास ऑडियो है, आइए इसे अपेक्षित प्रारूप में परिवर्तित करें और फिर इसे सुनें!

SPICE मॉडल को 16kHz के नमूने दर और केवल एक चैनल (मोनो) के साथ एक ऑडियो फ़ाइल इनपुट के रूप में चाहिए।

इस भाग के साथ आपकी सहायता करने के लिए, हमने किसी भी wav फ़ाइल को मॉडल के अपेक्षित प्रारूप में बदलने के लिए एक फ़ंक्शन ( convert_audio_for_model ) बनाया है:

# 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

पहली बात, आइए एक नज़र डालते हैं हमारे गायन की तरंग पर।

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

पींग

एक अधिक जानकारीपूर्ण दृश्य स्पेक्ट्रोग्राम है , जो समय के साथ मौजूद आवृत्तियों को दर्शाता है।

यहाँ, हम एक लघुगणक आवृत्ति पैमाने का उपयोग करते हैं, ताकि गायन अधिक स्पष्ट रूप से दिखाई दे।

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()
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/librosa/display.py:974: MatplotlibDeprecationWarning: The 'basey' parameter of __init__() has been renamed 'base' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
  scaler(mode, **kwargs)
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/librosa/display.py:974: MatplotlibDeprecationWarning: The 'linthreshy' parameter of __init__() has been renamed 'linthresh' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
  scaler(mode, **kwargs)
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/librosa/display.py:974: MatplotlibDeprecationWarning: The 'linscaley' parameter of __init__() has been renamed 'linscale' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
  scaler(mode, **kwargs)

पींग

हमें यहां एक अंतिम रूपांतरण की आवश्यकता है। ऑडियो नमूने int16 प्रारूप में हैं। उन्हें -1 और 1 के बीच तैरने के लिए सामान्यीकृत करने की आवश्यकता है।

audio_samples = audio_samples / float(MAX_ABS_INT16)

मॉडल को निष्पादित करना

अब आसान हिस्सा है, आइए TensorFlow Hub के साथ मॉडल को लोड करें, और ऑडियो को इसे खिलाएं। स्पाइस हमें दो आउटपुट देगा: पिच और अनिश्चितता

TensorFlow हब मशीन लर्निंग मॉडल के पुन: प्रयोज्य भागों के प्रकाशन, खोज और उपभोग के लिए एक पुस्तकालय है। अपनी चुनौतियों को हल करने के लिए मशीन लर्निंग का उपयोग करना आसान बनाता है।

मॉडल को लोड करने के लिए आपको बस हब मॉड्यूल और मॉडल की ओर इशारा करने वाले URL की आवश्यकता होती है:

02 बी 7 ए 97230
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().

लोड किए गए मॉडल के साथ, डेटा तैयार किया गया, परिणाम प्राप्त करने के लिए हमें 3 लाइनों की आवश्यकता है:

# 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()

पींग

आइए कम आत्मविश्वास (आत्मविश्वास <0.9) के साथ सभी पिच अनुमानों को हटाकर और शेष लोगों को प्लॉट करके परिणामों को समझना आसान बनाएं।

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()

पींग

SPICE द्वारा लौटाए गए पिच मान 0 से 1 तक के होते हैं। आइए इन्हें हेज़ में पूर्ण पिच मानों में परिवर्तित करें।

0 बी 8116 ए 470

अब, देखते हैं कि भविष्यवाणी कितनी अच्छी है: हम मूल स्पेक्ट्रोग्राम पर अनुमानित पिचों को ओवरले करेंगे। पिच की भविष्यवाणियों को अधिक दृश्यमान बनाने के लिए, हमने स्पेक्ट्रोग्राम को काले और सफेद में बदल दिया।

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()
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/librosa/display.py:974: MatplotlibDeprecationWarning: The 'basey' parameter of __init__() has been renamed 'base' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
  scaler(mode, **kwargs)
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/librosa/display.py:974: MatplotlibDeprecationWarning: The 'linthreshy' parameter of __init__() has been renamed 'linthresh' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
  scaler(mode, **kwargs)
/tmpfs/src/tf_docs_env/lib/python3.6/site-packages/librosa/display.py:974: MatplotlibDeprecationWarning: The 'linscaley' parameter of __init__() has been renamed 'linscale' since Matplotlib 3.3; support for the old name will be dropped two minor releases later.
  scaler(mode, **kwargs)

पींग

संगीत नोट्स के लिए परिवर्तित

अब जब हमारे पास पिच मूल्य हैं, तो उन्हें नोट्स में परिवर्तित करें! यह हिस्सा अपने आप में चुनौतीपूर्ण है। हमें दो बातों पर ध्यान देना होगा:

  1. टिकी हुई है (जब कोई गायन नहीं है)
  2. प्रत्येक नोट का आकार (ऑफसेट)

1: जब कोई गायन न हो, यह इंगित करने के लिए आउटपुट में शून्य जोड़ना

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

2: नोट ऑफसेट जोड़ना

जब कोई व्यक्ति स्वतंत्र रूप से गाता है, तो राग पूर्ण पिच मूल्यों के लिए एक ऑफसेट हो सकता है जो नोट्स का प्रतिनिधित्व कर सकता है। इसलिए, भविष्यवाणियों को नोट्स में बदलने के लिए, इस संभावित ऑफसेट के लिए सही करने की आवश्यकता है। यह निम्न कोड गणना है।

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, 0.3700387640422065, 0.2861639241998972, 0.19609005646164235, 0.17851549283916768, 0.2733467103665532, -0.4475316266590852, -0.24651997073237908, -0.1796576844031108, -0.23060136331860548, -0.3782634107643901, -0.4725081829601976, -0.3457232133919419, -0.2436666886383776, -0.1818906877810207, -0.1348058943325796, -0.24551812662426897, -0.4454884661609313, -0.3126792745167535, -0.12241723670307181, -0.06614479972665066, -0.06702634735648871, -0.1744135098034576, -0.29365739389006507, -0.32521078421418537, -0.0564402572685907, 0.1470525135224534, 0.17167006002122775, 0.16529246704037348, 0.09569531546290477, -0.0063254962736891684, -0.11800010039155495, -0.18835098459069144, -0.17934754504506145, -0.17215607120338916, -0.23695640070980062, -0.34594313039129077, -0.39380045278613807, -0.2528674895936689, -0.11009436621014146, -0.07118785365169344, -0.08042248799149121, -0.12799598588293293, -0.16227484329287023, -0.05931985421721464, 0.10667988763506742, 0.2104449983065848, 0.2931939382975841, -0.22329278631751492, -0.12365553720538003, -0.4571117360765271, -0.34864566459005175, -0.3594817457968418, -0.4313175396496476, -0.4818984494978622, 0.44220950977261, 0.45883109973128455, -0.47095522924010425, -0.3674495078498552, -0.3047186536962201, -0.31076167209689487, -0.4501401792341895, 0.396607746345353, 0.4238116671269694, 0.4982714279120728, -0.45931842459980743, -0.4890504510576079, 0.3836852730935334, 0.4441286145275285, -0.38787359430138935, -0.24855899466817277, -0.20666198684519088, -0.23811575664822726, -0.2760223047310504, -0.3641714288169524, -0.41670903606955534, -0.41009272976462086, -0.3340427999073796, -0.26122771753614416, -0.2232629008466489, -0.19940660549943345, -0.22528914465252825, -0.2780880208188776, -0.2744434134537457, -0.25655119194333764, -0.33068201704567457, -0.4678933079416083, -0.4695097918683686, -0.1648153518015647, -0.2461865211898413, -0.48052218123024204, -0.3771743489677135, -0.32261613680665846, -0.25560160024707557, -0.24629929913823645, -0.14035005553309787, -0.16659348412100883, -0.2442749349648139, -0.236980081337137, -0.20882882578912643, -0.22637331529204374, -0.2983651186401133, -0.39081296219175243, -0.3909915272766753, -0.3650093676025108, -0.2642347521955202, -0.13023199393098395, -0.18214744283501716, -0.3020849113041564, -0.33754041864220596, -0.34391801162306024, -0.31454499496763333, -0.26713502510135356, -0.2910458297902849, -0.11686573876684037, -0.1673094354445226, -0.24345334692542053, -0.30852622314040445, -0.35647376789395935, -0.37154466106240847, -0.3600149954730796, -0.2667062802488047, -0.21902188404148148, -0.2484456507736752, -0.2774107871825038, -0.2941432754570741, -0.31118778272216474, -0.32662896348779213, -0.3053947554403962, -0.2160201109821145, -0.17343703730647775, -0.17792559965198507, -0.19880455716197787, -0.2725068260604502, -0.3152120758468442, -0.28216813697164156, -0.11595223738495974, 0.0541902144377957, 0.11488166735824024, -0.2559679399306063, 0.01930235610660702, -0.002236352401425279, 0.44688340799267223, 0.15514959977323883, 0.4207713650291751, 0.3854436726992816, 0.4373497234409598, -0.4695032097274563, -0.3662756739431998, -0.20354085369650932, -0.015043790774988963, -0.4185651697093675, -0.17896653874461066, -0.032896162706066434, -0.061098168330843805, -0.1953772325689087, -0.2545161090666568, -0.3363741032654488, -0.39191536320988973, -0.36531668408458984, -0.3489657612020167, -0.35455202891175475, -0.38925380362813655, 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, -0.29791494323556833, -0.3228332309300086, -0.3895802937323367, 0.4323790387934068, 0.17438820408041522, -0.12961039467398905, -0.2236296109730489, -0.04022635205333813, -0.4264081214243589, -0.0019025255615048309, -0.07466309859101727, -0.08665327413623203, -0.08169104440753472, -0.31617519541327965, -0.47420548422877573, 0.1502044753855003, 0.30507923857624064, 0.031032583278971515, -0.17852388186996393, -0.3371385477358615, -0.41780861421172233, -0.2023970939094255, -0.10604901297633518, -0.10770872844999246, -0.16037790997569346, -0.18698410763089868, -0.17355977250879562, -0.008242337244190878, -0.011401999431292609, -0.18767393274848132, -0.360175323324853, 0.011681766969516616, -0.1931417836124183]
ideal offset:  -0.1688935487613971

अब हम नोट किए जाने वाले नोटों के सबसे संभावित अनुक्रम की कोशिश करने और अनुमान लगाने के लिए कुछ अनुमानों का उपयोग कर सकते हैं। ऊपर गणना की गई आदर्श ऑफसेट एक घटक है - लेकिन हमें गति (कितनी भविष्यवाणी करते हैं, कहते हैं, एक आठवें?) को जानने की भी आवश्यकता है, और मात्रा की शुरुआत करने के लिए समय ऑफसेट है। इसे सरल रखने के लिए, हम इस गति को कम करने वाले मानों का उपयोग करते हुए, अलग-अलग गति और समय की कोशिश करेंगे और परिमाणीकरण त्रुटि को मापेंगे।

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]

अब चलो मात्रात्मक नोटों को शीट संगीत स्कोर के रूप में लिखते हैं!

इसे करने के लिए हम दो पुस्तकालयों का उपयोग करेंगे: संगीत 21 और ओपन शीट म्यूजिक डिस्प्ले

# 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

[इसे चलाएं] म्यूजिक स्कोर दिखाने के लिए ओपन शीट म्यूजिक डिस्प्ले (जेएस कोड) का उपयोग करने के लिए हेल्पर फ़ंक्शन

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)
<IPython.core.display.Javascript object>
['C3', 'D3', 'E3', 'F3', 'G3', 'A3', 'B3', 'C4']

आइए संगीत नोटों को मिडी फ़ाइल में परिवर्तित करें और इसे सुनें।

इस फ़ाइल को बनाने के लिए, हम पहले बनाई गई स्ट्रीम का उपयोग कर सकते हैं।

# 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

कोलाब पर इसे सुनने के लिए, हमें इसे वापस wav में बदलने की आवश्यकता है। ऐसा करने का एक आसान तरीका है जो समयबद्धता का उपयोग कर रहा है।

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: 1  Divisions: 1024
Sequence: 
Playing time: ~16 seconds
Notes cut: 0
Notes lost totally: 0

और अंत में, मॉडल से अनुमानित, भविष्यवाणी की गई पिचों से मिडी के माध्यम से बनाए गए नोटों से बनाए गए ऑडियो को सुनें!

Audio(wav_from_created_midi)