import os
import argparse
import tqdm
import numpy as np

import collections
import contextlib
import sys
import wave
import webrtcvad
from multiprocessing import Pool
import argparse

def findAllSeqs(dirName,
                extension='.wav',
                speaker_level=1):
    r"""
    Lists all the sequences with the given extension in the dirName directory.
    Output:
        outSequences, speakers
        outSequence
        A list of tuples seq_path, speaker where:
            - seq_path is the relative path of each sequence relative to the
            parent directory
            - speaker is the corresponding speaker index
        outSpeakers
        The speaker labels (in order)
    The speaker labels are organized the following way
    \dirName
        \speaker_label
            \..
                ...
                seqName.extension
    Adjust the value of speaker_level if you want to choose which level of
    directory defines the speaker label. Ex if speaker_level == 2 then the
    dataset should be organized in the following fashion
    \dirName
        \crappy_label
            \speaker_label
                \..
                    ...
                    seqName.extension
    Set speaker_label == 0 if no speaker label will be retrieved no matter the
    organization of the dataset.
    """
    if dirName[-1] != os.sep:
        dirName += os.sep
    prefixSize = len(dirName)
    speakersTarget = {}
    outSequences = []
    print("finding {}, Waiting...".format(extension))
    for root, dirs, filenames in tqdm.tqdm(os.walk(dirName, followlinks=True)):
        filtered_files = [f for f in filenames if f.endswith(extension)]
        if len(filtered_files) > 0:
            speakerStr = (os.sep).join(
                root[prefixSize:].split(os.sep)[:speaker_level])
            if speakerStr not in speakersTarget:
                speakersTarget[speakerStr] = len(speakersTarget)
            speaker = speakersTarget[speakerStr]
            for filename in filtered_files:
                full_path = os.path.join(root, filename)
                outSequences.append((speaker, full_path))
    outSpeakers = [None for x in speakersTarget]

    for key, index in speakersTarget.items():
        outSpeakers[index] = key

    print("find {} speakers".format(len(outSpeakers)))
    print("find {} utterance".format(len(outSequences)))

    return outSequences, outSpeakers



def read_wave(path):
    """Reads a .wav file.

    Takes the path, and returns (PCM audio data, sample rate).
    """
    with contextlib.closing(wave.open(path, 'rb')) as wf:
        num_channels = wf.getnchannels()
        assert num_channels == 1
        sample_width = wf.getsampwidth()
        assert sample_width == 2
        sample_rate = wf.getframerate()
        assert sample_rate in (8000, 16000, 32000, 48000)
        pcm_data = wf.readframes(wf.getnframes())
        return pcm_data, sample_rate


def write_wave(path, audio, sample_rate):
    """Writes a .wav file.

    Takes path, PCM audio data, and sample rate.
    """
    with contextlib.closing(wave.open(path, 'wb')) as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(sample_rate)
        wf.writeframes(audio)


class Frame(object):
    """Represents a "frame" of audio data."""
    def __init__(self, bytes, timestamp, duration):
        self.bytes = bytes
        self.timestamp = timestamp
        self.duration = duration


def frame_generator(frame_duration_ms, audio, sample_rate):
    """Generates audio frames from PCM audio data.

    Takes the desired frame duration in milliseconds, the PCM data, and
    the sample rate.

    Yields Frames of the requested duration.
    """
    n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
    offset = 0
    timestamp = 0.0
    duration = (float(n) / sample_rate) / 2.0
    while offset + n < len(audio):
        yield Frame(audio[offset:offset + n], timestamp, duration)
        timestamp += duration
        offset += n


def vad_collector(sample_rate, frame_duration_ms,
                  padding_duration_ms, vad, frames):
    """Filters out non-voiced audio frames.

    Given a webrtcvad.Vad and a source of audio frames, yields only
    the voiced audio.

    Uses a padded, sliding window algorithm over the audio frames.
    When more than 90% of the frames in the window are voiced (as
    reported by the VAD), the collector triggers and begins yielding
    audio frames. Then the collector waits until 90% of the frames in
    the window are unvoiced to detrigger.

    The window is padded at the front and back to provide a small
    amount of silence or the beginnings/endings of speech around the
    voiced frames.

    Arguments:

    sample_rate - The audio sample rate, in Hz.
    frame_duration_ms - The frame duration in milliseconds.
    padding_duration_ms - The amount to pad the window, in milliseconds.
    vad - An instance of webrtcvad.Vad.
    frames - a source of audio frames (sequence or generator).

    Returns: A generator that yields PCM audio data.
    """
    num_padding_frames = int(padding_duration_ms / frame_duration_ms)
    # We use a deque for our sliding window/ring buffer.
    ring_buffer = collections.deque(maxlen=num_padding_frames)
    # We have two states: TRIGGERED and NOTTRIGGERED. We start in the NOTTRIGGERED state.
    triggered = False

    voiced_frames = []
    for frame in frames:
        is_speech = vad.is_speech(frame.bytes, sample_rate)

        sys.stdout.write('1' if is_speech else '0')
        if not triggered:
            ring_buffer.append((frame, is_speech))
            num_voiced = len([f for f, speech in ring_buffer if speech])
            # If we're NOTTRIGGERED and more than 90% of the frames in the ring buffer are voiced frames, then enter the
            # TRIGGERED state.
            if num_voiced > 0.9 * ring_buffer.maxlen:
                triggered = True
                sys.stdout.write('+(%s)' % (ring_buffer[0][0].timestamp,))
                # We want to yield all the audio we see from now until we are NOTTRIGGERED, but we have to start with the
                # audio that's already in the ring buffer.
                for f, s in ring_buffer:
                    voiced_frames.append(f)
                ring_buffer.clear()
        else:
            # We're in the TRIGGERED state, so collect the audio data and add it to the ring buffer.
            voiced_frames.append(frame)
            ring_buffer.append((frame, is_speech))
            num_unvoiced = len([f for f, speech in ring_buffer if not speech])
            # If more than 90% of the frames in the ring buffer are unvoiced, then enter NOTTRIGGERED and yield whatever
            # audio we've collected.
            if num_unvoiced > 0.9 * ring_buffer.maxlen:
                sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
                triggered = False
                yield b''.join([f.bytes for f in voiced_frames])
                ring_buffer.clear()
                voiced_frames = []
    if triggered:
        sys.stdout.write('-(%s)' % (frame.timestamp + frame.duration))
    sys.stdout.write('\n')
    # If we have any leftover voiced audio when we run out of input, yield it.
    if voiced_frames:
        yield b''.join([f.bytes for f in voiced_frames])


def vad_main(path):
    audio, sample_rate = read_wave(path)
    vad_path = path.strip(".wav") + ".vad"
    vad = webrtcvad.Vad(0)
    frames = frame_generator(30, audio, sample_rate)
    frames = list(frames)
    segments = vad_collector(sample_rate, 30, 300, vad, frames)
    for i, segment in enumerate(segments):
        write_wave(vad_path, segment, sample_rate)

if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('--data_dir', help='data dir', type=str, default="test_data/")
    parser.add_argument('--num_jobs', help='data dir', type=int, default=20)
    args = parser.parse_args()
    outSequences, _ = findAllSeqs(args.data_dir)
    data_paths = np.array(outSequences).T[1]

    with Pool(args.num_jobs) as p:
        p.map(vad_main, data_paths)



