Custom endpoint handler added + requirements.txt added

handler.py

@@ -0,0 +1,91 @@
+from typing import Dict, Any
+import tempfile
+import torchaudio
+import soundfile as sf
+import re
+from num2words import num2words
+from f5_tts.model import DiT
+from f5_tts.infer.utils_infer import (
+    load_vocoder,
+    load_model,
+    preprocess_ref_audio_text,
+    infer_process,
+    remove_silence_for_generated_wav,
+)
+import base64
+import io
+import numpy as np
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.vocoder = load_vocoder()
+        model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4)
+        model_path = "hf://jpgallegoar/F5-Spanish/model_1200000.safetensors"
+        self.ema_model = load_model(DiT, model_cfg, model_path)
+
+    def traducir_numero_a_texto(self, texto):
+        texto_separado = re.sub(r'([A-Za-z])(\d)', r'\1 \2', texto)
+        texto_separado = re.sub(r'(\d)([A-Za-z])', r'\1 \2', texto_separado)
+        def reemplazar_numero(match):
+            numero = match.group()
+            return num2words(int(numero), lang='es')
+        return re.sub(r'\b\d+\b', reemplazar_numero, texto_separado)
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        try:
+            ref_audio_path = data.get("ref_audio")
+            if not ref_audio_path:
+                return {"error": "Missing required field: 'ref_audio'"}
+
+            ref_text = data.get("ref_text", "")
+            gen_text = data.get("gen_text", "")
+            if not gen_text:
+                return {"error": "Missing required field: 'gen_text'"}
+
+            remove_silence = data.get("remove_silence", False)
+            cross_fade_duration = data.get("cross_fade_duration", 0.15)
+            speed = data.get("speed", 1.0)
+
+            ref_audio, ref_text = preprocess_ref_audio_text(ref_audio_path, ref_text, show_info=print)
+
+            if not gen_text.startswith(" "):
+                gen_text = " " + gen_text
+            if not gen_text.endswith(". "):
+                gen_text += ". "
+            gen_text = self.traducir_numero_a_texto(gen_text.lower())
+
+            final_wave, final_sample_rate, _ = infer_process(
+                ref_audio,
+                ref_text,
+                gen_text,
+                self.ema_model,
+                self.vocoder,
+                cross_fade_duration=cross_fade_duration,
+                speed=speed,
+                show_info=print,
+                progress=None,
+            )
+
+            if remove_silence:
+                with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+                    sf.write(f.name, final_wave, final_sample_rate)
+                    remove_silence_for_generated_wav(f.name)
+                    final_wave, _ = torchaudio.load(f.name)
+                final_wave = final_wave.squeeze().cpu().numpy()
+
+            with io.BytesIO() as buffer:
+                sf.write(buffer, final_wave, final_sample_rate, format="WAV")
+                buffer.seek(0)
+                encoded_audio = base64.b64encode(buffer.read()).decode("utf-8")
+
+            return {
+                "sucess": True,
+                "audio_base64": encoded_audio
+                }
+
+        except Exception as e:
+            return {
+                "success": False,
+                "error": f"{type(e).__name__}: {str(e)}"
+                }

requirements.txt

@@ -0,0 +1,9 @@
+torch
+torchaudio
+transformers
+numpy
+soundfile
+num2words
+cached_path
+openai-whisper
+git+https://github.com/jpgallegoar/Spanish-F5.git