break analysis off into class, and do threaded analysis
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222
main.py
222
main.py
@ -1,9 +1,12 @@
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import logging
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from typing import List, Dict
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import queue
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import pyaudio
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import numpy
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from numpy import pi
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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@ -12,90 +15,179 @@ standard_sample_rates = 1000 * numpy.array([
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8, 9.6, 11.025, 12, 16, 22.05, 24, 32,
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44.1, 48, 88.2, 96, 192])
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def monitor_pitch(device: int = 5,
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max_freq: float = 6000,
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min_freq: float = 10,
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samples_per_buffer: int = 1024,
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audio: pyaudio.PyAudio = None,
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):
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if audio is None:
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audio = pyaudio.PyAudio()
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note_names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
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def freq2note(freq: float):
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log_freq = numpy.log2(freq / 440)
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note = 12 * log_freq + 69
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base_note = numpy.round(note).astype(int)
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return log_freq, note, base_note
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def get_supported_sample_rates(pyaudio_device: int,
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pyaudio_object: pyaudio.PyAudio = None,
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) -> List[int]:
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if pyaudio_object is None:
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pyaudio_object = pyaudio.PyAudio()
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supported_sample_rates = []
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devinfo = audio.get_device_info_by_index(device)
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devinfo = pyaudio_object.get_device_info_by_index(device)
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for rate in standard_sample_rates:
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try:
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if audio.is_format_supported(rate,
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input_device=device,
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input_channels=devinfo['maxInputChannels'],
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input_format=pyaudio.paInt16):
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if pyaudio_object.is_format_supported(rate,
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input_device=device,
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input_channels=devinfo['maxInputChannels'],
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input_format=pyaudio.paInt16):
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supported_sample_rates.append(rate)
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except ValueError:
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pass
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supported_sample_rates = numpy.array(supported_sample_rates)
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logger.info('Supported rates: {}'.format(supported_sample_rates))
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'''
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max_freq < 2 * sample_rate
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min_freq * 2**(1/12) > freq_resolution (for discrimination), more for accuracy...
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freq_resolution <= sample_rate / (samples_per_buffer * num_buffers)
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'''
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freq_resolution = min_freq * 2**(1/12) / 10
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rate_is_acceptable = supported_sample_rates >= 2 * max_freq
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sample_rate = int(numpy.min(supported_sample_rates[rate_is_acceptable]))
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num_buffers = int(numpy.ceil(sample_rate / (samples_per_buffer * freq_resolution)))
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samples_per_fft = samples_per_buffer * num_buffers
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logger.info('Running on device {} with {} buffers,'.format(device, num_buffers) +
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' {} sample rate, {} samples per buffer'.format(
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device, num_buffers, sample_rate, samples_per_buffer))
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logger.info('Buffers take {:.3g} sec to fully clear'.format(samples_per_fft / sample_rate))
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stream = audio.open(format=pyaudio.paInt16,
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channels=1,
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rate=sample_rate,
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input=True,
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frames_per_buffer=samples_per_buffer)
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stream.start_stream()
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logger.info('Supported sample rates for device {}: {}'.format(device, supported_sample_rates))
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# Hanning window
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window = (1 - numpy.cos(numpy.linspace(0, 2 * pi, samples_per_fft, False))) / 2
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class AudioAnalyzer:
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frame_queue = None # type: queue.Queue
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freqs = numpy.fft.fftfreq(samples_per_fft, 1 / sample_rate)
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_hanning_window = None
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_fft_buffer = None
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_fft_lock = None
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_stream = None
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_pyaudio_object = None
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buf = numpy.zeros(num_buffers * samples_per_buffer, dtype=numpy.float32)
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note_names = ['C', 'C#', 'D', 'D#', 'E', 'F', 'F#', 'G', 'G#', 'A', 'A#', 'B']
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_fft_freqs = None
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_sample_rate = None
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_samples_per_buffer = None
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while stream.is_active():
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# Shift the buffer down and new data in
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buf[:-samples_per_buffer] = buf[samples_per_buffer:]
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buf[-samples_per_buffer:] = numpy.fromstring(stream.read(samples_per_buffer), numpy.int16)
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stop = None
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fft = numpy.fft.rfft(buf * window)
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def __init__(pyaudio_device: int,
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min_freq: float = 20,
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max_freq: float = 20e3,
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samples_per_buffer: int = 1024,
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freq_resolution: float = None,
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):
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# Get frequency of maximum response in range
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ind = numpy.abs(fft[1:]).argmax() + 1
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freq = freqs[ind]
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mag = numpy.abs(fft[ind])
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self._pyaudio_object = pyaudio.PyAudio()
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# Get note number and nearest note
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q = numpy.log2(freq/440)
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n = 12 * q + 69
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n0 = int(round(n))
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'''
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max_freq < 2 * sample_rate
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min_freq * 2**(1/12) > freq_resolution (for discrimination), more for accuracy...
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freq_resolution <= sample_rate / (samples_per_buffer * num_buffers)
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'''
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if freq_resolution is None:
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freq_resolution = min_freq * 2**(1/12) / 10
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delta = n - n0
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supported_sample_rates = get_supported_sample_rates(pyaudio_device, self._pyaudio_object)
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rate_is_acceptable = supported_sample_rates >= 2 * max_freq
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sample_rate = numpy.min(supported_sample_rates[rate_is_acceptable]).astype(int)
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num_buffers = numpy.ceil(sample_rate / (samples_per_buffer * freq_resolution)).astype(int)
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samples_per_fft = samples_per_buffer * num_buffers
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self._sample_rate = sample_rate
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self._samples_per_buffer = samples_per_buffer
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self._hanning_window = (1 - numpy.cos(numpy.linspace(0, 2 * pi, samples_per_fft, False))) / 2
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self._fft_freqs = numpy.fft.fftfreq(samples_per_fft, 1 / sample_rate)
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self._fft_buffer = numpy.zeros(num_buffers * samples_per_buffer, dtype=numpy.float32)
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self.stop = False
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self._fft_lock = threading.Lock()
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self.frame_queue = queue.Queue()
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self._stream = audio.open(format=pyaudio.paInt16,
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channels=1,
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rate=sample_rate,
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input=True,
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frames_per_buffer=samples_per_buffer,
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stream_callback=self.update)
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logger.info('Opened device {} with {} buffers,'.format(device, num_buffers) +
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' {} sample rate, {} samples per buffer'.format(
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device, num_buffers, sample_rate, samples_per_buffer))
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logger.info('Buffers take {:.3g} sec to fully clear'.format(samples_per_fft / sample_rate))
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@property
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def fft_freqs(self) -> float:
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return self._fft_freqs
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def start(self):
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self._stream.start_stream()
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def close(self):
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self.stop = True
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self._stream.close()
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self._pyaudio_object.terminate()
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def update(self,
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in_data: bytes,
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frame_count: int,
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time_info: Dict,
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status_flags,
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):
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#TODO deal with exceptions happening in the callback!
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in_buffer = numpy.fromstring(in_data, numpy.int16)
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samples_per_buffer = in_buffer.size
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with self._fft_lock:
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self._fft_buffer[:-samples_per_buffer] = self._fft_buffer[samples_per_buffer:]
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self._fft_buffer[-samples_per_buffer:] = in_buffer
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fft = numpy.fft.rfft(self._fft_buffer * self._hanning_window)
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fft_argmax = numpy.abs(fft[1:]).argmax() + 1 # excluding 0-frequency
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frame_data = {
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'fft': fft,
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'fft_argmax': fft_argmax,
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'frequency': self.fft_freqs[fft_argmax],
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'magnitude': numpy.abs(fft[fft_argmax]),
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}
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time_per_buffer = self._samples_per_buffer / self._sample_rate
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try:
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self.frame_queue.put(frame_data, timeout=time_per_buffer * 10)
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except queue.Full:
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logger.warning('Frame queue was full for more than 10 buffer periods!')
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if self.stop:
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return None, pyaudio.paComplete
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else:
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return None, pyaudio.paContinue
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def monitor_pitch(device: int = 5,
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min_freq: float = 10,
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max_freq: float = 6000,
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):
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analyzer = AudioAnalyzer(device=device,
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min_freq=min_freq,
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max_freq=max_freq)
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prev_magnitude = 0
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analyzer.start()
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while True:
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frame_data = analyzer.frame_queue.get()
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if frame_data['magnitude'] <= prev_magnitude / 2:
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continue
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prev_magnitude = frame_data['magnitude']
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_, mnote, mnote_base = freq2note(frame_data['frequency'])
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mnote_error = mnote - mnote_base
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logger.info('freq: {:7.2f} Hz mag:{:7.2f} note: {:>3s} {:+.2f}'.format(
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freq, numpy.log10(mag), note_names[n0 % 12] + str(n0//12 - 1), delta))
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freq, numpy.log10(mag), note_names[base_mnote % 12] + str(base_mnote//12 - 1), mnote_error))
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delta_part = int(delta // 0.1)
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if delta_part > 0:
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signal = ' ' * 6 + '+' * delta_part
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elif delta_part == 0:
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signal = ' ' * 5 + '|'
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elif delta_part < 0:
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signal = ' ' * (5 + delta_part) + '-' * delta_part
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max_num_symbols = 5
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num_symbols = int(mnote_error // (0.5 / max_num_symbols))
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if num_symbols > 0:
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signal = ' ' * max_num_symbols + ' ' + '+' * num_symbols
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elif num_symbols == 0:
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signal = ' ' * max_num_symbols + '|'
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elif num_symbols < 0:
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signal = ' ' * (max_num_symbols - num_symbols) + '-' * num_symbols
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logger.info(' {}'.format(signal))
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