Voice Cloning For Any Language | Fine-Tuning Tortoise-TTS | Part 2

2024-03-23 · Source: Martin Thissen · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

This content details the process of adapting the Tortoise-TTS architecture for custom language speech generation, specifically focusing on German. It outlines steps for uploading a fine-tuned autoregressive model to the Hugging Face Hub, a prerequisite for modifying the inference code. The process involves installing the Hugging Face Hub library, initializing the API with user credentials and repository details, and then creating a repository and uploading the fine-tuned model weights. Subsequently, the original Tortoise-TTS library's inference code is altered by cloning the repository, modifying the `tokenizer.py` file to include custom language cleaners and tokenizer paths, and updating the `api.py` file to load the fine-tuned autoregressive model from the Hugging Face Hub. The guide concludes with a demonstration of generating German speech using the adapted model and discusses post-processing techniques like speech trimming to improve audio quality.

Key takeaway

For AI Engineers adapting text-to-speech models for new languages, you should prioritize fine-tuning the autoregressive component and meticulously adjust the tokenizer and API loading paths. Ensure your custom tokenizer is saved and correctly placed, as the fine-tuned model's performance critically depends on it. Consider post-processing steps like amplitude-based trimming to refine output quality, especially when dealing with non-native language generation.

Key insights

Adapt Tortoise-TTS for custom languages by fine-tuning the autoregressive model and modifying inference code.

Principles

• Fine-tuned models require corresponding tokenizers.
• Input audio quality impacts generated speech quality.

Method

Upload fine-tuned autoregressive model to Hugging Face Hub, then modify `tokenizer.py` for custom cleaners and `api.py` to load the custom model for inference.

In practice

• Use `pip install huggingface_hub` for uploads.
• Create Hugging Face token with write permissions.
• Trim generated speech using amplitude analysis.

Topics

• Tortoise-TTS
• Custom Language Speech Synthesis
• Hugging Face Model Deployment
• Text-to-Speech Inference
• Model Fine-tuning

Best for: AI Engineer, Machine Learning Engineer, Deep Learning Engineer

Related on AIssential

• Voice Cloning For Any Language | Fine-Tuning Tortoise-TTS | Part 1 — Fine-tuning Tortoise-TTS for new languages requires custom data preparation, code modifications, and tokenizer training.
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• VoiceTTA: Enhancing Zero-Shot Text-to-Speech via Reinforcement Learning-Based Test-Time Adaptation — VoiceTTA enhances zero-shot TTS for uncommon styles via RL-based test-time adaptation, optimizing learnable prefixes with novel F0/energy style rewards.
• Covertly improving intelligibility with data-driven adaptations of speech timing — Targeted speech rate adjustments, not global slowing, covertly improve intelligibility across diverse listener groups.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Martin Thissen.