| --- |
| license: mit |
| language: |
| - zh |
| tags: |
| - text-to-speech |
| - evaluation |
| - webfm |
| --- |
| |
| # webfm-eval |
|
|
| Fixed evaluation set and harness for the WebFM TTS work. Small on purpose: it |
| exists so two model tiers can be compared on the same sentences in the same |
| run, not to be trained on. |
|
|
| ## Contents |
|
|
| ``` |
| eval/sentences.json 40 sentences, 7 buckets (short / medium / long / |
| number / latin / polyphone / narration) |
| eval/score.py raw + normalised CER, prefix CER, per-bucket and |
| per-voice aggregation, delta against a baseline |
| eval/t2s.json traditional -> simplified table, used only when |
| opencc is unavailable |
| config/ming_voices.json the eight WebFM voices and their prompt transcripts |
| refs/wav/v01..v08.wav prompt clips, reconstructed from the shipped F5 |
| reference mels |
| kaggle/bake_voices.py prompt_latent + CAM++ speaker_emb for each voice |
| kaggle/ming_eval.py teacher x student matrix, resumable per cell |
| ``` |
|
|
| ## Why the scoring is fussy |
|
|
| Whisper usually transcribes Chinese in traditional characters. Comparing that |
| against simplified references and counting character differences adds tens of |
| points of fake error. Every score here is reported twice: `cer_raw` (as |
| produced) and `cer_norm` (after traditional->simplified, punctuation stripped). |
| The gap is measurement, not model quality. |
|
|
| `cer_prefix` scores only the first four characters. A garbled opening syllable |
| barely moves overall CER but ruins a radio line, and it is the failure mode that |
| weight-only int4 quantisation produced on the 3-step student. |
|
|
| ## Usage |
|
|
| ```bash |
| python3 eval/score.py selftest |
| python3 eval/score.py aggregate --results results.jsonl --baseline teacher --by-bucket |
| ``` |
|
|
| `results.jsonl` carries one row per generated utterance: |
| `{id, model, voice, text, hyp_text, spk_cos?, mel_l1?, dur_ratio?}`. |
|
|
| ## Licence |
|
|
| MIT for the code. The prompt clips derive from a purchased SFW corpus; check the |
| source terms before commercial use. |
|
|
