[Python] Comparing WhisperX and Faster-Whisper on RunPod: Speed, Accuracy, and Optimization

Recently, I compared the performance of WhisperX and Faster-Whisper on RunPod's server using the following code snippet.

WhisperX


model = whisperx.load_model(
"large-v3", "cuda"
)

def run_whisperx_job(job):
start_time = time.time()

job_input = job['input']
url = job_input.get('url', "")

print(f" Loading audio from {url}...")
audio = whisperx.load_audio(url)
print("✅ Audio loaded")

print("Transcribing...")
result = model.transcribe(audio, batch_size=16)

end_time = time.time()
time_s = (end_time - start_time)
print(f" Transcription done: {time_s:.2f} s")
#print(result)

# For easy migration, we are following the output format of runpod's
# official faster whisper.
# https://github.com/runpod-workers/worker-faster_whisper/blob/main/src/predict.py#L111
output = {
'detected_language' : result['language'],
'segments' : result['segments']
}

return output

Faster-whisper


# Load Faster-Whisper model
model = WhisperModel("large-v3", device="cuda", compute_type="float16")

def run_faster_whisper_job(job):
start_time = time.time()

job_input = job['input']
url = job_input.get('url', "")

print(f" Downloading audio from {url}...")
audio_path = download_files_from_urls(job['id'], )[0] print("✅ Audio downloaded"...-accuracy-and-optimizati"]Continue reading...