license: apache-2.0
base_model: openai/whisper-small.en
library_name: basert
pipeline_tag: automatic-speech-recognition
tags:
- basert
- apple-silicon
- whisper
- speech-recognition
whisper-small.en
BaseRT .base build of
openai/whisper-small.en,
OpenAI's 244M-parameter Whisper speech-recognition model (English-only),
for fast local transcription on Apple Silicon.
Files
| File | Precision | Size |
|---|---|---|
whisper-small.en-F16.base |
float16 | 489 MB |
whisper-small.en-Q8.base |
8-bit linears, f16 embeddings/conv/norms | 297 MB |
whisper-small.en-Q4.base |
4-bit linears, f16 embeddings/conv/norms | 204 MB |
F16 and Q8 are transcription-quality equivalent (Q8 passes the same word-error parity gates against reference openai-whisper). Q4 is the smallest and remains accurate; on some smaller variants it can occasionally repeat a word in timestamped beam decoding.
Usage
curl -LsSf https://basecompute.co/install.sh | sh
basert serve --model whisper-small.en-F16.base
POST /v1/audio/transcriptions (multipart or JSON) returns json, text,
srt, vtt, or verbose_json (with per-segment avg_logprob /
no_speech_prob / compression_ratio / temperature and the detected
language), with optional SSE streaming. Supported
request fields: language (or "auto" to detect), prompt
(initial prompt / vocabulary bias). Or transcribe directly
from the CLI:
basert-transcribe whisper-small.en-F16.base audio.wav
This is the English-only variant; language is fixed to en and task=translate is rejected.
Released under the apache-2.0 license, inherited from the base model.