Upload 20221008 artifacts

.gitattributes

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+deepspeech-0.9.3-models-zh-TW.pbmm filter=lfs diff=lfs merge=lfs -text
+deepspeech-0.9.3-models-zh-TW.scorer filter=lfs diff=lfs merge=lfs -text
+deepspeech-0.9.3-models-zh-TW.tflite filter=lfs diff=lfs merge=lfs -text
+deepspeech-0.9.3-models.pbmm filter=lfs diff=lfs merge=lfs -text
+deepspeech-0.9.3-models.scorer filter=lfs diff=lfs merge=lfs -text
+deepspeech-0.9.3-models.tflite filter=lfs diff=lfs merge=lfs -text

.python-version

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+3.7

LICENSE

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+ 

deepspeech-0.9.3-models-zh-TW.pbmm

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mic_vad_streaming.py

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+import time, logging
+from datetime import datetime
+import threading, collections, queue, os, os.path
+import deepspeech
+import numpy as np
+import pyaudio
+import wave
+import webrtcvad
+from halo import Halo
+from scipy import signal
+
+logging.basicConfig(level=20)
+
+class Audio(object):
+    """Streams raw audio from microphone. Data is received in a separate thread, and stored in a buffer, to be read from."""
+
+    FORMAT = pyaudio.paInt16
+    # Network/VAD rate-space
+    RATE_PROCESS = 16000
+    CHANNELS = 1
+    BLOCKS_PER_SECOND = 50
+
+    def __init__(self, callback=None, device=None, input_rate=RATE_PROCESS, file=None):
+        def proxy_callback(in_data, frame_count, time_info, status):
+            #pylint: disable=unused-argument
+            if self.chunk is not None:
+                in_data = self.wf.readframes(self.chunk)
+            callback(in_data)
+            return (None, pyaudio.paContinue)
+        if callback is None: callback = lambda in_data: self.buffer_queue.put(in_data)
+        self.buffer_queue = queue.Queue()
+        self.device = device
+        self.input_rate = input_rate
+        self.sample_rate = self.RATE_PROCESS
+        self.block_size = int(self.RATE_PROCESS / float(self.BLOCKS_PER_SECOND))
+        self.block_size_input = int(self.input_rate / float(self.BLOCKS_PER_SECOND))
+        self.pa = pyaudio.PyAudio()
+
+        kwargs = {
+            'format': self.FORMAT,
+            'channels': self.CHANNELS,
+            'rate': self.input_rate,
+            'input': True,
+            'frames_per_buffer': self.block_size_input,
+            'stream_callback': proxy_callback,
+        }
+
+        self.chunk = None
+        # if not default device
+        if self.device:
+            kwargs['input_device_index'] = self.device
+        elif file is not None:
+            self.chunk = 320
+            self.wf = wave.open(file, 'rb')
+
+        self.stream = self.pa.open(**kwargs)
+        self.stream.start_stream()
+
+    def resample(self, data, input_rate):
+        """
+        Microphone may not support our native processing sampling rate, so
+        resample from input_rate to RATE_PROCESS here for webrtcvad and
+        deepspeech
+        Args:
+            data (binary): Input audio stream
+            input_rate (int): Input audio rate to resample from
+        """
+        data16 = np.fromstring(string=data, dtype=np.int16)
+        resample_size = int(len(data16) / self.input_rate * self.RATE_PROCESS)
+        resample = signal.resample(data16, resample_size)
+        resample16 = np.array(resample, dtype=np.int16)
+        return resample16.tostring()
+
+    def read_resampled(self):
+        """Return a block of audio data resampled to 16000hz, blocking if necessary."""
+        return self.resample(data=self.buffer_queue.get(),
+                             input_rate=self.input_rate)
+
+    def read(self):
+        """Return a block of audio data, blocking if necessary."""
+        return self.buffer_queue.get()
+
+    def destroy(self):
+        self.stream.stop_stream()
+        self.stream.close()
+        self.pa.terminate()
+
+    frame_duration_ms = property(lambda self: 1000 * self.block_size // self.sample_rate)
+
+    def write_wav(self, filename, data):
+        logging.info("write wav %s", filename)
+        wf = wave.open(filename, 'wb')
+        wf.setnchannels(self.CHANNELS)
+        # wf.setsampwidth(self.pa.get_sample_size(FORMAT))
+        assert self.FORMAT == pyaudio.paInt16
+        wf.setsampwidth(2)
+        wf.setframerate(self.sample_rate)
+        wf.writeframes(data)
+        wf.close()
+
+
+class VADAudio(Audio):
+    """Filter & segment audio with voice activity detection."""
+
+    def __init__(self, aggressiveness=3, device=None, input_rate=None, file=None):
+        super().__init__(device=device, input_rate=input_rate, file=file)
+        self.vad = webrtcvad.Vad(aggressiveness)
+
+    def frame_generator(self):
+        """Generator that yields all audio frames from microphone."""
+        if self.input_rate == self.RATE_PROCESS:
+            while True:
+                yield self.read()
+        else:
+            while True:
+                yield self.read_resampled()
+
+    def vad_collector(self, padding_ms=300, ratio=0.75, frames=None):
+        """Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
+            Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being triggered.
+            Example: (frame, ..., frame, None, frame, ..., frame, None, ...)
+                      |---utterence---|        |---utterence---|
+        """
+        if frames is None: frames = self.frame_generator()
+        num_padding_frames = padding_ms // self.frame_duration_ms
+        ring_buffer = collections.deque(maxlen=num_padding_frames)
+        triggered = False
+
+        for frame in frames:
+            if len(frame) < 640:
+                return
+
+            is_speech = self.vad.is_speech(frame, self.sample_rate)
+
+            if not triggered:
+                ring_buffer.append((frame, is_speech))
+                num_voiced = len([f for f, speech in ring_buffer if speech])
+                if num_voiced > ratio * ring_buffer.maxlen:
+                    triggered = True
+                    for f, s in ring_buffer:
+                        yield f
+                    ring_buffer.clear()
+
+            else:
+                yield frame
+                ring_buffer.append((frame, is_speech))
+                num_unvoiced = len([f for f, speech in ring_buffer if not speech])
+                if num_unvoiced > ratio * ring_buffer.maxlen:
+                    triggered = False
+                    yield None
+                    ring_buffer.clear()
+
+def main(ARGS):
+    # Load DeepSpeech model
+    if os.path.isdir(ARGS.model):
+        model_dir = ARGS.model
+        ARGS.model = os.path.join(model_dir, 'output_graph.pb')
+        ARGS.scorer = os.path.join(model_dir, ARGS.scorer)
+
+    print('Initializing model...')
+    logging.info("ARGS.model: %s", ARGS.model)
+    model = deepspeech.Model(ARGS.model)
+    if ARGS.scorer:
+        logging.info("ARGS.scorer: %s", ARGS.scorer)
+        model.enableExternalScorer(ARGS.scorer)
+
+    # Start audio with VAD
+    vad_audio = VADAudio(aggressiveness=ARGS.vad_aggressiveness,
+                         device=ARGS.device,
+                         input_rate=ARGS.rate,
+                         file=ARGS.file)
+    print("Listening (ctrl-C to exit)...")
+    frames = vad_audio.vad_collector()
+
+    # Stream from microphone to DeepSpeech using VAD
+    spinner = None
+    if not ARGS.nospinner:
+        spinner = Halo(spinner='line')
+    stream_context = model.createStream()
+    wav_data = bytearray()
+    for frame in frames:
+        if frame is not None:
+            if spinner: spinner.start()
+            logging.debug("streaming frame")
+            stream_context.feedAudioContent(np.frombuffer(frame, np.int16))
+            if ARGS.savewav: wav_data.extend(frame)
+        else:
+            if spinner: spinner.stop()
+            logging.debug("end utterence")
+            if ARGS.savewav:
+                vad_audio.write_wav(os.path.join(ARGS.savewav, datetime.now().strftime("savewav_%Y-%m-%d_%H-%M-%S_%f.wav")), wav_data)
+                wav_data = bytearray()
+            text = stream_context.finishStream()
+            print("Recognized: %s" % text)
+            stream_context = model.createStream()
+
+if __name__ == '__main__':
+    DEFAULT_SAMPLE_RATE = 16000
+
+    import argparse
+    parser = argparse.ArgumentParser(description="Stream from microphone to DeepSpeech using VAD")
+
+    parser.add_argument('-v', '--vad_aggressiveness', type=int, default=3,
+                        help="Set aggressiveness of VAD: an integer between 0 and 3, 0 being the least aggressive about filtering out non-speech, 3 the most aggressive. Default: 3")
+    parser.add_argument('--nospinner', action='store_true',
+                        help="Disable spinner")
+    parser.add_argument('-w', '--savewav',
+                        help="Save .wav files of utterences to given directory")
+    parser.add_argument('-f', '--file',
+                        help="Read from .wav file instead of microphone")
+
+    parser.add_argument('-m', '--model', required=True,
+                        help="Path to the model (protocol buffer binary file, or entire directory containing all standard-named files for model)")
+    parser.add_argument('-s', '--scorer',
+                        help="Path to the external scorer file.")
+    parser.add_argument('-d', '--device', type=int, default=None,
+                        help="Device input index (Int) as listed by pyaudio.PyAudio.get_device_info_by_index(). If not provided, falls back to PyAudio.get_default_device().")
+    parser.add_argument('-r', '--rate', type=int, default=DEFAULT_SAMPLE_RATE,
+                        help=f"Input device sample rate. Default: {DEFAULT_SAMPLE_RATE}. Your device may require 44100.")
+
+    ARGS = parser.parse_args()
+    if ARGS.savewav: os.makedirs(ARGS.savewav, exist_ok=True)
+    main(ARGS)

requirements.txt

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+deepspeech~=0.9.3
+pyaudio~=0.2.11
+halo~=0.0.18
+numpy>=1.15.1
+scipy>=1.1.0