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import os, json
import gradio as gr
from faster_whisper import WhisperModel
from moviepy.editor import VideoFileClip
def convert_video_to_audio(video_input):
video_clip = VideoFileClip(video_input)
audio_clip = video_clip.audio
audio_clip_filepath = os.path.normpath(f"{video_input.split('.')[0]}.m4a")
audio_clip.write_audiofile(audio_clip_filepath, codec='aac')
audio_clip.close()
video_clip.close()
return audio_clip_filepath
def convert_seconds_to_time(seconds):
seconds = float(seconds)
hours, remainder = divmod(seconds, 3600)
minutes, remainder = divmod(remainder, 60)
whole_seconds = int(remainder)
milliseconds = int((remainder - whole_seconds) * 1000)
return f"{int(hours):02}:{int(minutes):02}:{whole_seconds:02},{milliseconds:03}"
def write_srt(segments, max_words_per_line, srt_path):
with open(srt_path, "w", encoding='utf-8') as file:
result = ''
result_clean = []
json_output = {
"lines": []
}
line_counter = 1
for _, segment in enumerate(segments):
words_in_line = []
for w, word in enumerate(segment.words):
words_in_line.append(word)
# Write the line if max words limit reached or it's the last word in the segment
if len(words_in_line) == max_words_per_line or w == len(segment.words) - 1:
if words_in_line:
start_time = convert_seconds_to_time(words_in_line[0].start)
end_time = convert_seconds_to_time(words_in_line[-1].end)
line_text = ' '.join([w.word.strip() for w in words_in_line])
# SRT format
result += f"{line_counter}\n{start_time} --> {end_time}\n{line_text}\n\n"
result_clean += [line_text]
# JSON format
json_output["lines"].append({
"line_index": line_counter,
"start": words_in_line[0].start,
"end": words_in_line[-1].end,
"text": line_text,
"words": [
{
"word": w.word.strip(),
"start": w.start,
"end": w.end
} for w in words_in_line
]
})
line_counter += 1
words_in_line = []
file.write(result)
return result, srt_path, " ".join(result_clean), json.dumps(json_output)
def transcriber(file_input:gr.File,
file_type: str,
max_words_per_line:int,
task:str,
model_version:str):
srt_filepath = os.path.normpath(f"{file_input.split('.')[0]}.srt")
if file_type == "video" :
audio_input = convert_video_to_audio(file_input)
else:
audio_input = file_input
model = WhisperModel(model_version, device="cpu", compute_type="int8")
segments, _ = model.transcribe(
audio_input,
beam_size=5,
task=task,
vad_filter=True,
vad_parameters=dict(min_silence_duration_ms=500),
word_timestamps=True
)
return write_srt(segments=segments, max_words_per_line=max_words_per_line, srt_path=srt_filepath) |