#!/usr/bin/python3
"""Use OpenAI Whisper to automatically generate captions for the video files in the specified directory."""
# {{ ansible_managed }}
# The MIT License (MIT)
# Copyright © 2022 Sacha Chua <sacha@sachachua.com>
# Permission is hereby granted, free of charge, to any person
# obtaining a copy of this software and associated documentation files
# (the “Software”), to deal in the Software without restriction,
# including without limitation the rights to use, copy, modify, merge,
# publish, distribute, sublicense, and/or sell copies of the Software,
# and to permit persons to whom the Software is furnished to do so,
# subject to the following conditions:
# The above copyright notice and this permission notice shall be
# included in all copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,
# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS
# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN
# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from collections import defaultdict
import subprocess
import datetime
import sys
import webvtt
import xml.etree.ElementTree as ET
from lhotse import RecordingSet, Recording, AudioSource, SupervisionSegment, SupervisionSet, create_cut_set_eager, align_with_torchaudio, CutSet, annotate_with_whisper
import whisper
import re
import os
import json
import torch
THREADS = {{ cpus }}
VIDEO_REGEXP = '\.(webm|mov|mp4)$'
AUDIO_REGEXP = '\.(ogg|opus)$'
ALWAYS = False
TRIM_AUDIO = False
MODEL = os.environ.get('MODEL', 'large') # Set to tiny for testing
WORK_DIR = "{{ emacsconf_caption_dir }}"
JSON_FILE = os.path.join(WORK_DIR, 'talks.json')
# ----------------------------------------------------------------
def get_slug_from_filename(filename):
m = re.search('emacsconf-[0-9]+-([a-z]+)--', filename)
if m:
return m.group(1)
else:
return os.path.basename(os.path.dirname(filename))
def get_files_to_work_on(directory):
"""Return the list of audio files to work on.
The specified directory is checked recursively.
Skip any videos that already have caption files.
Convert any videos that don't already have audio files, and return the audio files instead.
When there are multiple videos and audio files for a talk, pick one.
"""
info = defaultdict(lambda: {}, {})
directory = os.path.expanduser(directory)
for folder, subs, files in os.walk(directory):
for filename in files:
f = os.path.join(folder, filename)
slug = get_slug_from_filename(f)
info[slug]['slug'] = slug
if re.search(AUDIO_REGEXP, filename):
info[slug]['audio'] = f
elif re.search(VIDEO_REGEXP, filename):
info[slug]['video'] = f
elif re.search('vtt$', filename):
info[slug]['vtt'] = f
elif re.search('srv2$', filename):
info[slug]['srv2'] = f
needs_work = []
if JSON_FILE:
with open(JSON_FILE) as f:
talks = json.load(f)['talks']
for key, val in info.items():
if not 'video' in val and not 'audio' in val: continue
if talks:
talk = next(filter(lambda talk: talk['slug'] == val['slug'], talks), None)
if talk:
val['base'] = os.path.join(os.path.dirname(val['video'] or val['audio']),
base_name(talk['video-slug']))
else:
val['base'] = os.path.join(os.path.dirname(val['video'] or val['audio']),
base_name(val['video'] or val['audio']))
if ALWAYS or (not 'vtt' in val or not 'srv2' in val):
if not 'audio' in val and 'video' in val:
# No audio, need to convert it
val = extract_audio(val)
needs_work.append(val)
return needs_work
def extract_audio(work):
output = subprocess.check_output(['ffprobe', work['video']], stderr=subprocess.STDOUT)
extension = 'opus'
if 'Audio: vorbis' in output.decode():
extension = 'ogg'
new_file = work['base'] + '.' + extension
acodec = 'copy' if re.search('webm$', work['video']) else 'libopus'
log("Extracting audio from %s acodec %s" % (work['video'], acodec))
output = subprocess.check_output(['ffmpeg', '-y', '-i', work['video'], '-acodec', acodec, '-vn', new_file], stderr=subprocess.STDOUT)
work['audio'] = new_file
return work
def to_sec(time_str):
"Convert a WebVTT time into seconds."
h, m, s, ms = re.split('[\\.:]', time_str)
return int(h) * 3600 + int(m) * 60 + int(s) + (int(ms) / 1000)
def log(s):
print(datetime.datetime.now(), s)
def clean_up_timestamps(result):
segs = list(result['segments'])
seg_len = len(segs)
for i, seg in enumerate(segs[:-1]):
seg['end'] = min(segs[i + 1]['start'] - 0.001, seg['end'])
result['segments'] = segs
return result
def generate_captions(work):
"""Generate a VTT file based on the audio file."""
log("Generating captions")
new_file = work['base'] + '.vtt'
model = whisper.load_model(MODEL, device="cuda" if torch.cuda.is_available() else "cpu")
audio = whisper.load_audio(work['audio'])
if TRIM_AUDIO:
audio = whisper.pad_or_trim(audio)
result = model.transcribe(audio, verbose=True)
result = clean_up_timestamps(result)
with open(new_file, 'w') as vtt:
whisper.utils.write_vtt(result['segments'], file=vtt)
work['vtt'] = new_file
if 'srv2' in work: del work['srv2']
return work
def generate_srv2(work):
"""Generate a SRV2 file."""
log("Generating SRV2")
recs = RecordingSet.from_recordings([Recording.from_file(work['audio'])])
rec_id = recs[0].id
captions = []
for i, caption in enumerate(webvtt.read(work['vtt'])):
if TRIM_AUDIO and i > 2: break
captions.append(SupervisionSegment(id=rec_id + '-sup' + '%05d' % i, channel=recs[0].channel_ids[0], recording_id=rec_id, start=to_sec(caption.start), duration=to_sec(caption.end) - to_sec(caption.start), text=caption.text, language='English'))
sups = SupervisionSet.from_segments(captions)
main = CutSet.from_manifests(recordings=recs, supervisions=sups)
work['cuts'] = main.trim_to_supervisions(keep_overlapping=False, keep_all_channels=True)
cuts_aligned = align_with_torchaudio(work['cuts'])
root = ET.Element("timedtext")
doc = ET.SubElement(root, "window")
for line, aligned in enumerate(cuts_aligned):
if len(aligned.supervisions) > 0:
aligned_words = aligned.supervisions[0].alignment['word']
for w, word in enumerate(aligned_words):
el = ET.SubElement(doc, 'text',
t=str(float(word.start)*1000),
d=str(float(word.duration)*1000),
w="1",
append="1")
el.text = word.symbol
el.tail = "\n"
else:
print("No supervisions", aligned)
tree = ET.ElementTree(root)
work['srv2'] = work['base'] + '.srv2'
with open(work['srv2'], "w") as f:
tree.write(f.buffer)
return work
def base_name(s):
"""
Return the base name of file so that we can add extensions to it.
Remove tokens like --normalized, --recoded, etc.
Make sure the filename has either --main or --questions.
"""
s = os.path.basename(s)
type = 'questions' if '--questions.' in s else 'main'
if TRIM_AUDIO:
type = 'test'
match = re.match('^(emacsconf-[0-9]+-[a-z]+--.*?--.*?)(--|\.)', s)
if (match):
return match.group(1) + '--' + type
else:
return os.path.splitext(s)[0] + '--' + type
# assert(base_name('/home/sachac/current/sqlite/emacsconf-2022-sqlite--using-sqlite-as-a-data-source-a-framework-and-an-example--andrew-hyatt--normalized.webm.vtt') == 'emacsconf-2022-sqlite--using-sqlite-as-a-data-source-a-framework-and-an-example--andrew-hyatt--main')
log(f"MODEL {MODEL} ALWAYS {ALWAYS} TRIM_AUDIO {TRIM_AUDIO}")
directory = sys.argv[1] if len(sys.argv) > 1 else WORK_DIR
needs_work = get_files_to_work_on(directory)
if len(needs_work) > 0:
while len(needs_work) > 0:
if THREADS > 0:
torch.set_num_threads(THREADS)
for work in needs_work:
log("Started processing %s" % work['base'])
if work['audio']:
if ALWAYS or not 'vtt' in work:
work = generate_captions(work)
if ALWAYS or not 'srv2' in work:
work = generate_srv2(work)
# print("Aligning words", audio_file, datetime.datetime.now())
# word_cuts = align_words(cuts)
# convert_cuts_to_word_timing(audio_file, word_cuts)
log("Done %s" % str(work['base']))
needs_work = get_files_to_work_on(directory)
else:
log("No work needed.")
