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from faster_whisper import WhisperModel
import logging, os
logging.basicConfig(filename='main.log',
encoding='utf-8',
level=logging.DEBUG,
format='%(asctime)s %(levelname)s %(message)s',
datefmt='%m/%d/%Y %I:%M:%S %p')
logging.getLogger("faster_whisper").setLevel(logging.DEBUG)
def write_srt(segments, srt_path, max_words_per_line):
"""Write segments to an SRT file with a maximum number of words per line."""
with open(srt_path, "w", encoding='utf-8') as file:
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: # Check to avoid writing a line if there are no words
start_time = words_in_line[0].start
end_time = words_in_line[-1].end
line_text = ' '.join([w.word.strip() for w in words_in_line])
file.write(f"{line_counter}\n{start_time} --> {end_time}\n{line_text}\n\n")
# Reset for the next line and increment line counter
line_counter += 1
words_in_line = [] # Reset words list for the next line
def transcriber(input_path:str,
srt_path:str,
max_words_per_line:int):
model_size = "large-v3"
# Run on GPU with FP16
# model = WhisperModel(model_size, device="cuda", compute_type="float16")
# or run on GPU with INT8
# model = WhisperModel(model_size, device="cuda", compute_type="int8_float16")
# or run on CPU with INT8
logging.info("Logging Whisper model...")
model = WhisperModel(model_size, device="cpu", compute_type="int8")
logging.info("Starting transcription...")
segments, info = model.transcribe(
input_path,
beam_size=5,
vad_filter=True,
vad_parameters=dict(min_silence_duration_ms=500),
word_timestamps=True
)
logging.info("Detected language '%s' with probability %f" % (info.language, info.language_probability))
logging.info("Writing file...")
write_srt(segments=segments, srt_path=srt_path, max_words_per_line=max_words_per_line) |