whisper

Extract word level timing in Whisper using the word_timestamps flag or API setting. Learn how to precisely align audio with text for accurate timing information.

Discover how OpenAI Whisper processes audio and generates mel spectrograms using its four-stage pipeline including ffmpeg loading STFT computation and Mel filterbank projection.

**KV caching reduces Whisper's autoregressive decoding complexity from O(T²) to O(T) by reusing previously computed key and value tensors across generation steps, eliminating redundant attention calculations during long audio t...

Get granular control over Whisper's output by learning how to enable and customize word-level timestamps using the Python API or CLI. Fine-tune punctuation and silence settings for precise transcriptions.

**You can detect the spoken language in an audio file using Whisper by calling `model.detect_language()` for a lightweight encoder-only check, or by setting `task="lang_id"` in `DecodingOptions` to use the high-level decoding A...

**The `model.transcribe()` method returns a Python dictionary containing three top-level keys: `text` (the full transcription string), `segments` (a list of per-chunk dictionaries with timestamps and metadata), and `language` (...

**Use `initial_prompt` to inject static text at the start of transcription, and enable `condition_on_previous_text` (default: True) to carry decoded output from previous audio windows into subsequent decoding steps for contextu...

Explore advanced Whisper model.transcribe() parameters like temperature and thresholds. Optimize transcription accuracy with detailed sampling and validation controls.

**The `model.transcribe()` function is a high-level wrapper that orchestrates audio preprocessing, language detection, windowed decoding with temperature fallback, and timestamp extraction to convert speech into structured text...

**Whisper automatically generates segment-level timestamps for every transcription, and you can activate word-level precision by passing `word_timestamps=True` to the `transcribe()` function or using the `--word_timestamps` CLI...

Learn how to suppress specific tokens or blank outputs in Whisper decoding. Configure DecodingOptions with suppress_blank and suppress_tokens for cleaner results.

**To configure OpenAI Whisper for greedy decoding, set `temperature=0.0` and leave `beam_size=None` in the `DecodingOptions` dataclass; for beam search, provide an integer value (e.g., `5`) to `beam_size` and set `temperature=0...