faster-whisper

# faster-whisper 1.2.x · Python >=3.9
pip install faster-whisper

# transcribe.py
from faster_whisper import WhisperModel

# device="cpu" + compute_type="int8" also works
model = WhisperModel(
    "large-v3-turbo",      # or large-v3, medium, small, tiny
    device="cuda",
    compute_type="float16",
)

segments, info = model.transcribe(
    "audio.mp3",
    beam_size=5,
)
print(f"detected language: {info.language} (p={info.language_probability:.2f})")
for s in segments:
    print(f"[{s.start:6.2f} -> {s.end:6.2f}] {s.text}")

# CUDA + cuDNN already installed on the box.
# For CUDA 12, cuDNN 9 is bundled via nvidia-cudnn-cu12.
pip install faster-whisper nvidia-cudnn-cu12

from faster_whisper import WhisperModel, BatchedInferencePipeline

model = WhisperModel(
    "large-v3-turbo",
    device="cuda",
    compute_type="float16",   # int8_float16 halves VRAM
)

# batched = parallel decode across 30s chunks
batched = BatchedInferencePipeline(model=model)

segments, info = batched.transcribe(
    "podcast.wav",
    batch_size=16,            # 8 on 8 GB, 16 on 16 GB, 32 on 24 GB+
    beam_size=5,
)
for s in segments:
    print(s.start, s.end, s.text)

from faster_whisper import WhisperModel

model = WhisperModel("large-v3", device="cuda", compute_type="float16")

segments, info = model.transcribe(
    "meeting.wav",
    beam_size=5,
    word_timestamps=True,
    vad_filter=True,
    vad_parameters=dict(
        min_silence_duration_ms=500,   # drop silences >=500ms
    ),
)

for s in segments:
    print(f"[{s.start:6.2f} -> {s.end:6.2f}] {s.text}")
    for w in s.words:
        print(f"  {w.start:6.2f}-{w.end:6.2f}  {w.word!r}")