# -*- coding: utf-8 -*- """tp3__1_-1.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1_Sjx5G1BW689ggZJAJ4P7kCZndOobNCp """ # Install Gradio #!pip install gradio -q # Install timidy #!sudo apt-get install -q -y timidity libsndfile1 # All the imports to deal with sound data #!pip install pydub numba==0.48 librosa music21 # Import Libraries import gradio as gr import time 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__) # The audio input file # Now the hardest part: Record your singing! :) # We provide four methods to obtain an audio file: # 1. Record audio directly in Gradio # 2. Use a file saved on Google Drive # Use a file saved on Google Drive #INPUT_SOURCE = 'https://storage.googleapis.com/download.tensorflow.org/data/c-scale-metronome.wav' #!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' #uploaded_file_name # 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 # Funciones # 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 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') return fig # Loading audio samples from the wav file: sample_rate, audio_samples = wavfile.read(converted_audio_file, 'rb') fig = plot_stft(audio_samples / MAX_ABS_INT16 , sample_rate=EXPECTED_SAMPLE_RATE) # Executing the Model # Loading the SPICE model is easy: model = hub.load("https://tfhub.dev/google/spice/2") def plot_pitch_conf(pitch_outputs,confidence_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") return fig def plot_pitch_conf_notes(confident_pitch_outputs_x,confident_pitch_outputs_y): 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") return fig 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) def espectro_notas(audio_samples,EXPECTED_SAMPLE_RATE,confident_pitch_outputs_x,confident_pitch_values_hz): fig, ax = plt.subplots() 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") return fig 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 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 def main(audio): # Preparing the audio data # Now we have the audio, let's convert it to the expected format and then # listen to it! # The SPICE model needs as input an audio file at a sampling rate of 16kHz and # with only one channel (mono). # To help you with this part, we created a function(`convert_audio_for_model`) #to convert any wav file you have to the model's expected format: # 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(audio) # Loading audio samples from the wav file: sample_rate, audio_samples = wavfile.read(converted_audio_file, 'rb') audio_samples = audio_samples / float(MAX_ABS_INT16) # 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 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) confident_pitch_values_hz = [ output2hz(p) for p in confident_pitch_outputs_y ] #Plot waves fig1 = plt.figure() plt.plot(audio_samples) #Plot fig2 = plot_stft(audio_samples / MAX_ABS_INT16 , sample_rate=EXPECTED_SAMPLE_RATE) #Plot Pitch & Confidence fig3 = plot_pitch_conf(pitch_outputs,confidence_outputs) #Plot Pitch & Confidence Notes fig4 = plot_pitch_conf_notes(confident_pitch_outputs_x,confident_pitch_outputs_y) #Plot Espectro + Notes fig5 = espectro_notas(audio_samples,EXPECTED_SAMPLE_RATE,confident_pitch_outputs_x,confident_pitch_values_hz) # ############################################################################ # Converting to musical notes ################################################ # Now that we have the pitch values, let's convert them to notes! # This is part is challenging by itself. We have to take into account two # things: # 1. the rests (when there's no singing) # 2. the size of each note (offsets) # ---------------------------------------------------------------------------- ### 1: Adding zeros to the output to indicate when there's no singing pitch_outputs_and_rests = [ output2hz(p) if c >= 0.9 else 0 for i, p, c in zip(indices, pitch_outputs, confidence_outputs) ] # ---------------------------------------------------------------------------- ### 2: Adding note offsets # When a person sings freely, the melody may have an offset to the absolute # pitch values that notes can represent. # Hence, to convert predictions to notes, one needs to correct for this # possible offset. # This is what the following code computes. 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) off = offsets ideal_offset = statistics.mean(offsets) #print("ideal offset: ", ideal_offset) ideal_off = ideal_offset # We can now use some heuristics to try and estimate the most likely sequence # of notes that were sung. # The ideal offset computed above is one ingredient - but we also need to know # the speed (how many predictions make, say, an eighth?), and the time offset # to start quantizing. To keep it simple, we'll just try different speeds and # time offsets and measure the quantization error, using in the end the values # that minimize this 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] # ____________________________________________________________________________ # Now let's write the quantized notes as sheet music score! # To do it we will use two libraries: [music21](http://web.mit.edu/music21/) and # [Open Sheet Music Display](https://github.com/opensheetmusicdisplay/opensheetmusicdisplay) # **Note:** for simplicity, we assume here that all notes have the same duration # (a half note). # 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)) # @title [Run this] Helper function to use Open Sheet Music Display (JS code) # to show a music score 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" a = display(HTML('
loading OpenSheetMusicDisplay
')) 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 a # rendering the music score partitura = showScore(sc) #print(best_notes_and_rests) # ____________________________________________________________________________ # Let's convert the music notes to a MIDI file and listen to it. # To create this file, we can use the stream we created before. # 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) # To listen to it on colab, we need to convert it back to wav. An easy way of # doing that is using Timidity. #!timidity $converted_audio_file_as_midi -Ow -o $wav_from_created_midi #return converted_audio_file, fig1, fig2, fig3, fig4,fig5, bpm, best_notes_and_rests, partitura, wav_from_created_midi link = "https://www.tensorflow.org/hub/tutorials/spice?hl=es-419&authuser=2" iface = gr.Interface( fn=main, title= "Trabajo Práctico N°3 - Detección de tono con SPICE", description="Implementación de Modelo con GitHub + Hugging Face🤗-- 🔊✅ " + "Basado en: " + link, inputs = [gr.inputs.Audio(source= "microphone" , type="filepath",label="Ingrese Audio")], outputs= [gr.outputs.Audio(label="Audio Original"), gr.outputs.Plot(type="auto",label="Gráfico de Frecuencias"), gr.outputs.Plot(type="auto",label="Especto"), gr.outputs.Plot(type="auto",label="Pitch Confidence"), gr.outputs.Plot(type="auto",label="Notas"), gr.outputs.Plot(type="auto",label="Espectro+Notas"), gr.outputs.Textbox(label="bpm"), gr.outputs.Textbox(label="partitura"), gr.outputs.Textbox(type="html",label="partitura1"), gr.outputs.Audio(label="midi")], interpretation = "default", ) iface.launch(debug=True)