Détecter et enregistrer l'audio dans Python

Question

Je dois capturer des clips audio sous forme de fichiers WAV que je peux ensuite transmettre à un autre élément de python pour traitement. Le problème est que je dois déterminer quand un fichier audio est présent, puis l'enregistrer, puis arrêter. puis il passe le fichier au module de traitement.

Je pense qu'il devrait être possible avec le module wave de détecter quand il y a un silence pur et de le supprimer dès que quelque chose d'autre que le silence est détecté, pour commencer l'enregistrement, puis quand la ligne redevient silencieuse, arrêtez l'enregistrement.

Je n'arrive tout simplement pas à comprendre ce qui se passe. Quelqu'un peut-il commencer avec un exemple élémentaire?.

Answer 1

Pour donner suite à la réponse de Nick Fortescue, voici un exemple plus complet expliquant comment enregistrer à partir du microphone et traiter les données obtenues:

from sys import byteorder
from array import array
from struct import pack

import pyaudio
import wave

THRESHOLD = 500
CHUNK_SIZE = 1024
FORMAT = pyaudio.Paint16
RATE = 44100

def is_silent(snd_data):
    "Returns 'True' if below the 'silent' threshold"
    return max(snd_data) < THRESHOLD

def normalize(snd_data):
    "Average the volume out"
    MAXIMUM = 16384
    times = float(MAXIMUM)/max(abs(i) for i in snd_data)

    r = array('h')
    for i in snd_data:
        r.append(int(i*times))
    return r

def trim(snd_data):
    "Trim the blank spots at the start and end"
    def _trim(snd_data):
        snd_started = False
        r = array('h')

        for i in snd_data:
            if not snd_started and abs(i)>THRESHOLD:
                snd_started = True
                r.append(i)

            Elif snd_started:
                r.append(i)
        return r

    # Trim to the left
    snd_data = _trim(snd_data)

    # Trim to the right
    snd_data.reverse()
    snd_data = _trim(snd_data)
    snd_data.reverse()
    return snd_data

def add_silence(snd_data, seconds):
    "Add silence to the start and end of 'snd_data' of length 'seconds' (float)"
    r = array('h', [0 for i in xrange(int(seconds*RATE))])
    r.extend(snd_data)
    r.extend([0 for i in xrange(int(seconds*RATE))])
    return r

def record():
    """
    Record a Word or words from the microphone and 
    return the data as an array of signed shorts.

    Normalizes the audio, trims silence from the 
    start and end, and pads with 0.5 seconds of 
    blank sound to make sure VLC et al can play 
    it without getting chopped off.
    """
    p = pyaudio.PyAudio()
    stream = p.open(format=FORMAT, channels=1, rate=RATE,
        input=True, output=True,
        frames_per_buffer=CHUNK_SIZE)

    num_silent = 0
    snd_started = False

    r = array('h')

    while 1:
        # little endian, signed short
        snd_data = array('h', stream.read(CHUNK_SIZE))
        if byteorder == 'big':
            snd_data.byteswap()
        r.extend(snd_data)

        silent = is_silent(snd_data)

        if silent and snd_started:
            num_silent += 1
        Elif not silent and not snd_started:
            snd_started = True

        if snd_started and num_silent > 30:
            break

    sample_width = p.get_sample_size(FORMAT)
    stream.stop_stream()
    stream.close()
    p.terminate()

    r = normalize(r)
    r = trim(r)
    r = add_silence(r, 0.5)
    return sample_width, r

def record_to_file(path):
    "Records from the microphone and outputs the resulting data to 'path'"
    sample_width, data = record()
    data = pack('<' + ('h'*len(data)), *data)

    wf = wave.open(path, 'wb')
    wf.setnchannels(1)
    wf.setsampwidth(sample_width)
    wf.setframerate(RATE)
    wf.writeframes(data)
    wf.close()

if __== '__main__':
    print("please speak a Word into the microphone")
    record_to_file('demo.wav')
    print("done - result written to demo.wav")

Answer 2

Je crois que le module WAVE ne prend pas en charge l’enregistrement, mais uniquement le traitement de fichiers existants. Vous voudrez peut-être regarder PyAudio pour réellement enregistrer. WAV concerne le format de fichier le plus simple au monde. Dans Paint16, vous obtenez simplement un entier signé représentant un niveau, et plus proche de 0, plus silencieux. Je ne me souviens pas si les fichiers WAV sont à octet haut ou octet bas, mais quelque chose comme ceci devrait fonctionner (désolé, je ne suis pas vraiment un programmeur python:

from array import array

# you'll probably want to experiment on threshold
# depends how noisy the signal
threshold = 10 
max_value = 0

as_ints = array('h', data)
max_value = max(as_ints)
if max_value > threshold:
    # not silence

PyAudio code pour l'enregistrement gardé pour référence:

import pyaudio
import sys

chunk = 1024
FORMAT = pyaudio.Paint16
CHANNELS = 1
RATE = 44100
RECORD_SECONDS = 5

p = pyaudio.PyAudio()

stream = p.open(format=FORMAT,
                channels=CHANNELS, 
                rate=RATE, 
                input=True,
                output=True,
                frames_per_buffer=chunk)

print "* recording"
for i in range(0, 44100 / chunk * RECORD_SECONDS):
    data = stream.read(chunk)
    # check for silence here by comparing the level with 0 (or some threshold) for 
    # the contents of data.
    # then write data or not to a file

print "* done"

stream.stop_stream()
stream.close()
p.terminate()

Answer 3

Merci à cryo pour la version améliorée de mon code testé ci-dessous:

#Instead of adding silence at start and end of recording (values=0) I add the original audio . This makes audio sound more natural as volume is >0. See trim()
#I also fixed issue with the previous code - accumulated silence counter needs to be cleared once recording is resumed.

from array import array
from struct import pack
from sys import byteorder
import copy
import pyaudio
import wave

THRESHOLD = 500  # audio levels not normalised.
CHUNK_SIZE = 1024
SILENT_CHUNKS = 3 * 44100 / 1024  # about 3sec
FORMAT = pyaudio.Paint16
FRAME_MAX_VALUE = 2 ** 15 - 1
NORMALIZE_MINUS_ONE_dB = 10 ** (-1.0 / 20)
RATE = 44100
CHANNELS = 1
TRIM_APPEND = RATE / 4

def is_silent(data_chunk):
    """Returns 'True' if below the 'silent' threshold"""
    return max(data_chunk) < THRESHOLD

def normalize(data_all):
    """Amplify the volume out to max -1dB"""
    # MAXIMUM = 16384
    normalize_factor = (float(NORMALIZE_MINUS_ONE_dB * FRAME_MAX_VALUE)
                        / max(abs(i) for i in data_all))

    r = array('h')
    for i in data_all:
        r.append(int(i * normalize_factor))
    return r

def trim(data_all):
    _from = 0
    _to = len(data_all) - 1
    for i, b in enumerate(data_all):
        if abs(b) > THRESHOLD:
            _from = max(0, i - TRIM_APPEND)
            break

    for i, b in enumerate(reversed(data_all)):
        if abs(b) > THRESHOLD:
            _to = min(len(data_all) - 1, len(data_all) - 1 - i + TRIM_APPEND)
            break

    return copy.deepcopy(data_all[_from:(_to + 1)])

def record():
    """Record a Word or words from the microphone and 
    return the data as an array of signed shorts."""

    p = pyaudio.PyAudio()
    stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, output=True, frames_per_buffer=CHUNK_SIZE)

    silent_chunks = 0
    audio_started = False
    data_all = array('h')

    while True:
        # little endian, signed short
        data_chunk = array('h', stream.read(CHUNK_SIZE))
        if byteorder == 'big':
            data_chunk.byteswap()
        data_all.extend(data_chunk)

        silent = is_silent(data_chunk)

        if audio_started:
            if silent:
                silent_chunks += 1
                if silent_chunks > SILENT_CHUNKS:
                    break
            else: 
                silent_chunks = 0
        Elif not silent:
            audio_started = True              

    sample_width = p.get_sample_size(FORMAT)
    stream.stop_stream()
    stream.close()
    p.terminate()

    data_all = trim(data_all)  # we trim before normalize as threshhold applies to un-normalized wave (as well as is_silent() function)
    data_all = normalize(data_all)
    return sample_width, data_all

def record_to_file(path):
    "Records from the microphone and outputs the resulting data to 'path'"
    sample_width, data = record()
    data = pack('<' + ('h' * len(data)), *data)

    wave_file = wave.open(path, 'wb')
    wave_file.setnchannels(CHANNELS)
    wave_file.setsampwidth(sample_width)
    wave_file.setframerate(RATE)
    wave_file.writeframes(data)
    wave_file.close()

if __== '__main__':
    print("Wait in silence to begin recording; wait in silence to terminate")
    record_to_file('demo.wav')
    print("done - result written to demo.wav")

Answer 4

import pyaudio
import wave
from array import array

FORMAT=pyaudio.Paint16
CHANNELS=2
RATE=44100
CHUNK=1024
RECORD_SECONDS=15
FILE_NAME="RECORDING.wav"

audio=pyaudio.PyAudio() #instantiate the pyaudio

#recording prerequisites
stream=audio.open(format=FORMAT,channels=CHANNELS, 
                  rate=RATE,
                  input=True,
                  frames_per_buffer=CHUNK)

#starting recording
frames=[]

for i in range(0,int(RATE/CHUNK*RECORD_SECONDS)):
    data=stream.read(CHUNK)
    data_chunk=array('h',data)
    vol=max(data_chunk)
    if(vol>=500):
        print("something said")
        frames.append(data)
    else:
        print("nothing")
    print("\n")


#end of recording
stream.stop_stream()
stream.close()
audio.terminate()
#writing to file
wavfile=wave.open(FILE_NAME,'wb')
wavfile.setnchannels(CHANNELS)
wavfile.setsampwidth(audio.get_sample_size(FORMAT))
wavfile.setframerate(RATE)
wavfile.writeframes(b''.join(frames))#append frames recorded to file
wavfile.close()

Je pense que cela aidera. C'est un script simple qui vérifie s'il y a un silence ou non. Si le silence est détecté, il n'enregistrera pas, sinon il enregistrera.

Answer 5

Le site Web pyaudio contient de nombreux exemples assez courts et clairs: http://people.csail.mit.edu/hubert/pyaudio/

Answer 6

Vous voudrez peut-être regarder csounds , également. Il a plusieurs API, y compris Python. Il pourrait peut-être interagir avec une interface A-D et rassembler des échantillons sonores.