Update README.md

README.md

@@ -18,7 +18,6 @@ pipeline_tag: text-to-speech
 
 A Swahili text-to-speech model, finetuned from Meta's [MMS-TTS Swahili](https://huggingface.co/facebook/mms-tts-swh) checkpoint on the `swa_tts` split of [google/WaxalNLP](https://huggingface.co/datasets/google/WaxalNLP), using the [VITS finetuning recipe](https://github.com/ylacombe/finetune-hf-vits) from `ylacombe/finetune-hf-vits`.
 
-> **⚠️ Early checkpoint.** This model is saved from step 4,500 of a planned 20,200-step (200-epoch) training run — roughly 22% through. Training was interrupted by free-tier Google Colab resource limits and is being continued on different hardware. Expect rough prosody and occasional mispronunciations. This card will be updated as training progresses.
 
 ## Model details
 
@@ -64,7 +63,6 @@ Research and experimentation with Swahili TTS, particularly cross-lingual/low-re
 
 ## Limitations
 
-- **Undertrained**: only ~22 epochs completed of a 200-epoch plan; expect unstable prosody, occasional artifacts, and inconsistent pacing.
 - **Single speaker, single dataset**: trained on one speaker's recordings from one dataset; may not generalize well to varied Swahili dialects, accents, or speaking styles.
 - **No formal evaluation yet**: no MOS, WER, or MCD scores have been computed for this checkpoint. Qualitative spot-checks against WAXAL's held-out `test` split suggest intelligible but rough output.
 - **Small dataset**: ~800 training utterances after filtering is a small corpus for TTS; may overfit to the training speaker's specific vocal characteristics.