src/dia_tts.py

"""
Dia TTS model integration for TTS Gallery
Based on: https://github.com/nari-labs/dia/blob/main/hf.py
"""

import tempfile
import torch
import soundfile as sf
# from transformers import AutoProcessor, DiaForConditionalGeneration

# class DiaTTS:
#     """
#     Wrapper for the Dia TTS model from Nari Labs
#     """
#     def __init__(self, model_checkpoint="nari-labs/Dia-1.6B"):
#         """
#         Initialize the Dia TTS model
#
#         Args:
#             model_checkpoint (str): HuggingFace model checkpoint to use
#         """
#         self.model_checkpoint = model_checkpoint
#         self.device = "cuda" if torch.cuda.is_available() else "cpu"
#
#         # Load processor and model
#         self.processor = AutoProcessor.from_pretrained(model_checkpoint)
#         self.model = DiaForConditionalGeneration.from_pretrained(model_checkpoint).to(self.device)
#
#         # Default generation parameters
#         self.generation_params = {
#             "max_new_tokens": 3072,
#             "guidance_scale": 3.0,
#             "temperature": 1.8,
#             "top_p": 0.90,
#             "top_k": 45
#         }
#
#     def generate(self, text, audio_prompt=None):
#         """
#         Generate speech from text using Dia
#
#         Args:
#             text (str): Text to convert to speech. Should use [S1] and [S2] tags for dialogue.
#             audio_prompt (str, optional): Path to reference audio file for voice cloning
#
#         Returns:
#             numpy.ndarray: Generated audio as a numpy array
#             int: Sample rate (44100)
#         """
#         # Format text with speaker tags if not already present
#         if not text.startswith("[S1]") and not text.startswith("[S2]"):
#             text = f"[S1] {text}"
#
#         # Prepare inputs
#         inputs = self.processor(text=[text], padding=True, return_tensors="pt").to(self.device)
#
#         # Generate audio
#         outputs = self.model.generate(**inputs, **self.generation_params)
#
#         # Decode outputs
#         audio_data = self.processor.batch_decode(outputs)
#
#         # Return audio data (assuming it's a numpy array) and sample rate
#         return audio_data[0], 44100  # Dia uses 44.1kHz sample rate
#
#     def generate_to_file(self, text, audio_prompt=None):
#         """
#         Generate speech from text and save to a temporary file
#
#         Args:
#             text (str): Text to convert to speech
#             audio_prompt (str, optional): Path to reference audio file for voice cloning
#
#         Returns:
#             str: Path to the generated audio file
#         """
#         audio_data, sample_rate = self.generate(text, audio_prompt)
#
#         # Save to a temporary file
#         with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp_file:
#             sf.write(tmp_file.name, audio_data, sample_rate)
#             return tmp_file.name