Example #1
0
def classify_htk(audio_file):
    MIN_BREATH_DUR = 0.1
    MIN_AC = 500

    cwd = os.getcwd()
    os.chdir(os.path.dirname(os.path.realpath(__file__)))
    
    transcript = "breath.transcript"
    output = "breath-classify-output.json"
    results = "tmp/aligned.results"

    with open(transcript, 'w') as f:
        f.write("""[{"speaker": "speaker", "line": "{BR}"}]""")

    do_alignment(audio_file, transcript, output, json=True, textgrid=False)

    os.remove(transcript)

    # subprocess.call('python ../p2fa/align.py ../%s %s %s' %
    #     (audio_file, transcript, output), shell=True)
    
    final_words = [{
        "start": 0.0,
        "end": 0.0,
        "alignedWord": "sp",
        "word": "{p}"
    }]
    
    with open(results, 'r') as res:
        match = ac_re.search(res.read())
        if match:
            ac = int(match.group(1))
            print "Ac:", ac
            if ac > MIN_AC:
                print "Breath!"
    
                with open(output, 'r') as out:
                    words = json.load(out)["words"]
                    breath = filter(
                        lambda x: x["alignedWord"] == "{BR}",
                        words)[0]
                    breath_dur = breath["end"] - breath["start"]
                    print "breath len", breath_dur
                    if breath_dur > MIN_BREATH_DUR:
                        final_words = words
                        final_words[0]["start"] = 0.0
                        for word in final_words:
                            if word["alignedWord"] == "{BR}":
                                word["likelihood"] = ac

    os.chdir(cwd)

    return final_words
Example #2
0
def align_transcripts(as_ep_ids):
    """
    Align the transcript segments in ./alignment_data/seg_json/ to episode
    audio using p2fa. If there are any errors during alignment, skip the
    episode on which they occur and move on.

    Parameters
    ----------
    as_ep_ids : list
        The list of all audiosearch episode ids associated with a particular
        podcast

    Returns
    -------
    as_ep_ids : list
        A list of episode ids that have been completely aligned.

    problem_episodes : list
        A list of episode ids that were not fully aligned
    """
    problem_episodes = []

    for ep_num in as_ep_ids:
        wavfile = './alignment_data/seg_audio/{}_seg*.wav'.format(ep_num)

        for ff in glob.glob(wavfile):
            file_name = os.path.split(ff)[-1].split('.')[0]

            trsfile = './alignment_data/seg_json/{}.json'.format(file_name)
            outfile = './alignment_data/alignments_json/' \
                      '{}_aligned.json'.format(file_name)

            if os.path.lexists(outfile):
                fn = os.path.split(outfile)[-1]
                print('\t{} already exists! Skipping'.format(fn))
                continue

            try:
                print('\n\tAligning {}...'.format(file_name))
                align.do_alignment(ff,
                                   trsfile,
                                   outfile,
                                   json=True,
                                   textgrid=False,
                                   phonemes=True,
                                   breaths=False)
            except:
                print('\n\tError Aligning {}'.format(file_name))
                problem_episodes.append(ep_num)
                as_ep_ids.remove(ep_num)
                continue

    return as_ep_ids, problem_episodes
def align_transcripts(as_ep_ids):
    """
    Align the transcript segments in ./alignment_data/seg_json/ to episode
    audio using p2fa. If there are any errors during alignment, skip the
    episode on which they occur and move on.

    Parameters
    ----------
    as_ep_ids : list
        The list of all audiosearch episode ids associated with a particular
        podcast

    Returns
    -------
    as_ep_ids : list
        A list of episode ids that have been completely aligned.

    problem_episodes : list
        A list of episode ids that were not fully aligned
    """
    problem_episodes = []

    for ep_num in as_ep_ids:
        wavfile = './alignment_data/seg_audio/{}_seg*.wav'.format(ep_num)

        for ff in glob.glob(wavfile):
            file_name = os.path.split(ff)[-1].split('.')[0]

            trsfile = './alignment_data/seg_json/{}.json'.format(file_name)
            outfile = './alignment_data/alignments_json/' \
                      '{}_aligned.json'.format(file_name)

            if os.path.lexists(outfile):
                fn = os.path.split(outfile)[-1]
                print('\t{} already exists! Skipping'.format(fn))
                continue

            try:
                print('\n\tAligning {}...'.format(file_name))
                align.do_alignment(ff, trsfile, outfile, json=True,
                                   textgrid=False, phonemes=True,
                                   breaths=False)
            except:
                print('\n\tError Aligning {}'.format(file_name))
                problem_episodes.append(ep_num)
                as_ep_ids.remove(ep_num)
                continue

    return as_ep_ids, problem_episodes