harpertoken
/

talk

@@ -15,9 +15,9 @@ tags:
 A `whisper-small` encoder-decoder, fine-tuned on short passages of live microphone audio, for English speech recognition at 16 kHz mono. The weights are float32 and occupy roughly 967 MB. The fine-tuning was slight: across 37 sampled encoder and decoder tensors the relative L2 distance from `openai/whisper-small` has a median of 0.0013 and a maximum of 0.024, with none exceeding 5%. That is a light touch consistent with a small amount of in-domain audio, and it is why this is best treated as `whisper-small` with a nudge rather than as a model of its own. The audio it was tuned on is not published, so the corpus itself remains a matter of record rather than something you can check here.
-Two corrections to the previous version of this card, both of which pointed at the wrong model. The metadata claimed a Wav2Vec2 lineage — `facebook/wav2vec2-base-960h` as a base model, and `facebook/multilingual_librispeech` as the training set. Neither is true of the published checkpoint: `config.json` records `_name_or_path: openai/whisper-small` with `WhisperForConditionalGeneration`, and the documented training signal was live recordings, not a fixed corpus. The earlier card also described `main.py`, `train.py`, `test_transcription.py` and a `requirements.txt`; none of those files have ever been in this repository, so the instructions it gave could not be followed.
-The tokenizer and the feature extractor are now present in the repository, taken from [`openai/whisper-small`](https://huggingface.co/openai/whisper-small) — the same 51865-token vocabulary and 80-bin mel filterbank the checkpoint expects, since that is what it was fine-tuned from. An earlier version of this repository shipped neither, and `AutoProcessor.from_pretrained` failed outright while `AutoTokenizer` returned a one-token vocabulary. Both are fixed; `WhisperForConditionalGeneration`, `AutoProcessor` and the `automatic-speech-recognition` pipeline now all load from this identifier alone. The repository's own `generation_config.json` was kept as fine-tuned rather than replaced with the upstream one. A duplicate `pytorch_model.bin` and a 761-byte `training_log.pt` left over from debugging were removed.
 ## Usage
@@ -47,7 +47,7 @@ print(transcriber(audio, return_timestamps=True))
 ## Limitations
-The fine-tuning corpus is a small quantity of live microphone audio — one room, one machine, 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.
 ## Attribution

 A `whisper-small` encoder-decoder, fine-tuned on short passages of live microphone audio, for English speech recognition at 16 kHz mono. The weights are float32 and occupy roughly 967 MB. The fine-tuning was slight: across 37 sampled encoder and decoder tensors the relative L2 distance from `openai/whisper-small` has a median of 0.0013 and a maximum of 0.024, with none exceeding 5%. That is a light touch consistent with a small amount of in-domain audio, and it is why this is best treated as `whisper-small` with a nudge rather than as a model of its own. The audio it was tuned on is not published, so the corpus itself remains a matter of record rather than something you can check here.
+Two corrections to the previous version of this card, both of which pointed at the wrong model. The metadata claimed a Wav2Vec2 lineage, naming `facebook/wav2vec2-base-960h` as a base model and `facebook/multilingual_librispeech` as the training set. Neither is true of the published checkpoint: `config.json` records `_name_or_path: openai/whisper-small` with `WhisperForConditionalGeneration`, and the documented training signal was live recordings, not a fixed corpus. The earlier card also described `main.py`, `train.py`, `test_transcription.py` and a `requirements.txt`; none of those files have ever been in this repository, so the instructions it gave could not be followed.
+The tokenizer and the feature extractor are now present in the repository, taken from [`openai/whisper-small`](https://huggingface.co/openai/whisper-small), which supplies the same 51865-token vocabulary and 80-bin mel filterbank the checkpoint expects, since that is what it was fine-tuned from. An earlier version of this repository shipped neither, and `AutoProcessor.from_pretrained` failed outright while `AutoTokenizer` returned a one-token vocabulary. Both are fixed; `WhisperForConditionalGeneration`, `AutoProcessor` and the `automatic-speech-recognition` pipeline now all load from this identifier alone. The repository's own `generation_config.json` was kept as fine-tuned rather than replaced with the upstream one. A duplicate `pytorch_model.bin` and a 761-byte `training_log.pt` left over from debugging were removed.
 ## Usage
 ## Limitations
+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.
 ## Attribution