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config.json

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+{}

handler.py

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+from typing import Dict
+from pathlib import Path
+import tempfile
+import torch
+import torchaudio
+import librosa
+
+SAMPLE_RATE = 16000
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Load the MARS5 model
+        self.mars5, self.config_class = torch.hub.load('Camb-ai/mars5-tts', 'mars5_english', trust_repo=True)
+
+    def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
+        """
+        Args:
+            data (Dict[str, bytes]):
+                Includes the text, audio file path, and transcript.
+        Returns:
+            Dict[str, str]: Path to the synthesized audio file.
+        """
+        # Process input
+        text = data["text"]
+        audio_file = data["audio_file"]
+        transcript = data["transcript"]
+
+        # Load the reference audio
+        wav, sr = librosa.load(audio_file, sr=self.mars5.sr, mono=True)
+        wav = torch.from_numpy(wav)
+
+        # Define the configuration for the TTS model
+        deep_clone = True
+        cfg = self.config_class(deep_clone=deep_clone, rep_penalty_window=100, top_k=100, temperature=0.7, freq_penalty=3)
+
+        # Generate the synthesized audio
+        ar_codes, wav_out = self.mars5.tts(text, wav, transcript, cfg=cfg)
+
+        # Save the synthesized audio to a temporary file
+        output_path = Path(tempfile.mktemp(suffix=".wav"))
+        torchaudio.save(output_path, wav_out.unsqueeze(0), self.mars5.sr)
+
+        return {"synthesized_audio": str(output_path)}