GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
harpertoken
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
harpertoken
Model Tree
openai/whisper-small
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import WhisperForConditionalGeneration, AutoProcessor, pipeline model = WhisperForConditionalGeneration.from_pretrained("harpertoken/talk")processor = AutoProcessor.from_pretrained("harpertoken/talk") transcriber = pipeline( "automatic-speech-recognition", model=model, tokenizer=processor.tokenizer, feature_extractor=processor.feature_extractor, torch_dtype=torch.float32,) # Supply 16 kHz mono float32 samples as a numpy array. The pipeline rejects a# placeholder such as (0, 16000): it raises TypeError on a tuple. Load real audio# with soundfile or librosa, for example:# audio, sample_rate = sf.read("clip.wav", dtype="float32")# assert sample_rate == 16000audio = load_audio() # your loader, returning np.ndarray of shape (n,)print(transcriber(audio, return_timestamps=True))The fine-tuning corpus is a small quantity of live microphone audio, from one room, one machine and likely one speaker, so this should be read as an adaptation of whisper-small rather than an independent model. I have no transcription accuracy measurement for it: the card this replaces listed a character error rate with no evaluation behind it, and that has been removed. Expect degradation on accents, noisy rooms, far-field audio and any language other than English. If you need a dependable transcriber, start from openai/whisper-small, whose published WER figures you can actually check.
Whisper follows Radford et al., Robust Speech Recognition via Large-Scale Weak Supervision (2022).