Developing a text-to-speech (TTS) model for a new language requires three primary resources: high-quality linguistic data, computational infrastructure, and domain-specific expertise. Each component plays a critical role in ensuring the model can accurately generate natural-sounding speech while accounting for the unique phonetic, syntactic, and cultural nuances of the target language. Below, we break down these requirements in detail.
First, linguistic data is foundational. You need a large, well-annotated dataset of spoken audio paired with corresponding text transcripts. For a new language, this might involve recording native speakers reading diverse texts (e.g., news articles, stories, or dialogues) to capture variations in pronunciation, intonation, and rhythm. A minimum of 20–40 hours of high-fidelity audio is typical for training a baseline model, though more data improves quality. The transcripts must be time-aligned to the audio (using tools like Praat or Montreal Forced Aligner) and include metadata such as speaker demographics (age, gender, dialect) to support multi-speaker models. For languages with limited digital resources, creating this dataset may require collaboration with local communities or institutions.
Second, computational resources are essential for training and optimizing the model. Modern neural TTS systems like Tacotron, FastSpeech, or VITS require significant GPU power, often involving clusters of high-end NVIDIA GPUs (e.g., A100 or H100) or cloud-based services (AWS, Google Cloud). Training a single model can take days or weeks, depending on architecture and dataset size. Software frameworks like TensorFlow, PyTorch, or domain-specific toolkits like ESPnet or Coqui TTS are needed to implement the model. Preprocessing steps, such as text normalization (handling numbers, abbreviations) and phoneme conversion, may also require custom scripts or tools like eSpeak-NG for grapheme-to-phoneme rules, especially for languages without existing phonetic dictionaries.
Finally, expertise in linguistics and machine learning is critical. Developers must understand the target language’s phonological structure (e.g., tone systems in Mandarin or click consonants in Xhosa) to design appropriate model inputs and loss functions. For example, tonal languages require embedding tone markers into the training data, while agglutinative languages (e.g., Turkish) may need subword tokenization. Collaboration with native speakers or linguists helps identify edge cases, such as dialectal variations or rare phonemes. Post-training, rigorous evaluation—using metrics like Mean Opinion Score (MOS) and listener surveys—ensures the model meets usability standards. Iterative refinement, driven by community feedback, is often necessary to address gaps in coverage or naturalness.