evaluation refactor

[VocodexElysium]

✨ Description

This pull request includes an overall refactor to the Amphion Evaluation module for faster inference speed and more computational modes for compatibility, which includes:

• CUDA support: Previously, all metrics are computed on the CPU. However, the computational speed will be incompatible with large-scale test sets that contain more than 10000 utterances. Currently, except for MCD and PESQ, which utilize totally encapsulated external packages, all the other metrics can now be computed on the GPU device to speed up.
• Support for computing intelligibility-related metrics with the ground truth transcript: Previously, CER and WER are computed on the transcript of an ASR model between the ground truth and the predicted audio. However, the ground truth transcript is available for tasks like TTS. Currently, it is possible to compute CER and WER between the ground truth transcript and the output of applying the ASR model on predicted audio.
• Support for similarity computation without reference or ground truth: Previously, speaker similarity is computed between a series of ground truth/prediction audios. However, such audio pairs may not be available for tasks like SVC. Currently, speaker similarity can be computed in a reference-free way that calculates the average score between all possible audio pairs in the reference and generated folder.

🚧 Related Issues

110

👨‍💻 Changes Proposed

• ✅ CUDA support
• ✅ Intelligibility-related metrics refactored
• ✅ Similarity-related metrics refactored
• ✅ Algorithm speed optimization
• ✅ Bug fix

🧑‍🤝‍🧑 Who Can Review?

@lmxue @HeCheng0625 @zhizhengwu

✅ Checklist

• ✅ Code has been reviewed
• ✅ Code complies with the project's code standards and best practices
• ✅ Code has passed all tests
• ✅ Code does not affect the normal use of existing features
• ✅ Code has been commented properly
• ✅ Documentation has been updated (if applicable)

[VocodexElysium]

Please refine for WavLM features extraction. Support for both huggingface pretrained model and offline model.

support added