Update README.md

README.md

@@ -1,3 +1,22 @@
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-license: apache-2.0
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+---
+license: apache-2.0
+datasets:
+- k2-fsa/TTS_eval_datasets
+language:
+- en
+- zh
+pipeline_tag: text-to-speech
+---
+
+This repository consists of various models for objective evaluation of text-to-speech (TTS) models:
+
+- **WER**: Includes [Hubert-based ASR model](https://huggingface.co/facebook/hubert-large-ls960-ft) for LibriSpeech-PC testset, [Paraformer-based ASR model](https://huggingface.co/funasr/paraformer-zh) for Chinese datasets, [Whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) model for general English test sets, [WhisperD](https://huggingface.co/jordand/whisper-d-v1a) model for English dialogue speech.
+
+- **cpWER**: [WhisperD](https://huggingface.co/jordand/whisper-d-v1a) model is used to compute concatenated minimum permutation word error rate
+([cpWER](https://arxiv.org/abs/2507.09318)) for English dialogue speech.
+
+- **SIM-o**: A [wavlm-based speaker verification model](https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification) is used to compute the speaker similarity between prompt and generated speech.
+
+- **cpSIM**: A [speaker diarization model](https://huggingface.co/pyannote/speaker-diarization-3.1) is used along with the above wavlm-based model to compute concatenated maximum permutation speaker similarity ([cpSIM](https://arxiv.org/abs/2507.09318)).
+
+- **UTMOS**: The mos prediction model [UTMOS](https://github.com/sarulab-speech/UTMOS22) is used.