Instructions to use k2-fsa/TTS_eval_models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use k2-fsa/TTS_eval_models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="k2-fsa/TTS_eval_models")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("k2-fsa/TTS_eval_models", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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pipeline_tag: text-to-speech
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This repository consists of
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- **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.
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pipeline_tag: text-to-speech
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This repository consists of models for objective evaluation of text-to-speech (TTS) models in [ZipVoice](https://arxiv.org/abs/2506.13053) and [ZipVoice-Dialog](https://arxiv.org/abs/2507.09318):
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- **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.
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