Update app.py

app.py

@@ -1,58 +1,124 @@
+# import os
+# import uuid
+# import time
+# import torch
+# import gradio as gr
+# os.environ["NUMBA_DISABLE_CACHE"] = "1"
+# import mecab_patch
+# import english_patch
+# from melo.api import TTS
+# from openvoice.api import ToneColorConverter
+
+# # Set temporary cache locations for Hugging Face Spaces
+# os.environ["TORCH_HOME"] = "/tmp/torch"
+# os.environ["HF_HOME"] = "/tmp/huggingface"
+# os.environ["HF_HUB_CACHE"] = "/tmp/huggingface"
+# os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
+# os.environ["MPLCONFIGDIR"] = "/tmp"
+# os.environ["XDG_CACHE_HOME"] = "/tmp"
+# os.environ["XDG_CONFIG_HOME"] = "/tmp"
+# os.environ["NUMBA_DISABLE_CACHE"] = "1"
+# os.makedirs("/tmp/torch", exist_ok=True)
+# os.makedirs("/tmp/huggingface", exist_ok=True)
+# os.makedirs("/tmp/flagged", exist_ok=True)
+
+# # Output folder
+# output_dir = "/tmp/outputs"
+# os.makedirs(output_dir, exist_ok=True)
+
+# # Initialize tone converter
+# ckpt_converter = "checkpoints/converter/config.json"
+# tone_color_converter = ToneColorConverter(ckpt_converter)
+
+# # Device setting
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# def clone_and_speak(text, speaker_wav):
+#     if not speaker_wav:
+#         return "Please upload a reference .wav file."
+
+#     # import melo.text.english as english
+#     # original_g2p = english.g2p
+
+#     # def patched_g2p(text):
+#     #     phones, tones, word2ph = original_g2p(text)
+#     #     # Fix: wrap ints in list to avoid TypeError
+#     #     word2ph_fixed = []
+#     #     for item in word2ph:
+#     #         if isinstance(item, int):
+#     #             word2ph_fixed.append([item])
+#     #         else:
+#     #             word2ph_fixed.append(item)
+#     #     return phones, tones, word2ph_fixed
+
+#     # english.g2p = patched_g2p
+
+#     base_name = f"output_{int(time.time())}_{uuid.uuid4().hex[:6]}"
+#     tmp_melo_path = f"{output_dir}/{base_name}_tmp.wav"
+#     final_output_path = f"{output_dir}/{base_name}_converted.wav"
+
+#     # Use English speaker model
+#     model = TTS(language="EN", device=device)
+#     speaker_ids = model.hps.data.spk2id
+#     default_speaker_id = next(iter(speaker_ids.values()))
+
+#     # Generate base TTS voice
+#     speed = 1.0
+#     model.tts_to_file(text, default_speaker_id, tmp_melo_path,speed=speed)
+
+#     # Use speaker_wav as reference to extract style embedding
+#     from openvoice import se_extractor
+#     ref_se, _ = se_extractor.get_se(speaker_wav, tone_color_converter, vad=False)
+
+#     # Run the tone conversion
+#     tone_color_converter.convert(
+#         audio_src_path=tmp_melo_path,
+#         src_se=ref_se,
+#         tgt_se=ref_se,
+#         output_path=final_output_path,
+#         message="@HuggingFace",
+#     )
+
+#     return final_output_path
+
+# # Gradio interface
+# gr.Interface(
+#     fn=clone_and_speak,
+#     inputs=[
+#         gr.Textbox(label="Enter Text"),
+#         gr.Audio(type="filepath", label="Upload a Reference Voice (.wav)")
+#     ],
+#     outputs=gr.Audio(label="Synthesized Output"),
+#     flagging_dir="/tmp/flagged",
+#     title="Text to Voice using Melo TTS + OpenVoice",
+#     description="Use Melo TTS for base synthesis and OpenVoice to apply a reference speaker's tone.",
+# ).launch()
+
+
 import os
-import uuid
 import time
-import torch
+import uuid
 import gradio as gr
-os.environ["NUMBA_DISABLE_CACHE"] = "1"
-import mecab_patch
-import english_patch
-from melo.api import TTS
-from openvoice.api import ToneColorConverter
-
-# Set temporary cache locations for Hugging Face Spaces
-os.environ["TORCH_HOME"] = "/tmp/torch"
-os.environ["HF_HOME"] = "/tmp/huggingface"
-os.environ["HF_HUB_CACHE"] = "/tmp/huggingface"
-os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
-os.environ["MPLCONFIGDIR"] = "/tmp"
-os.environ["XDG_CACHE_HOME"] = "/tmp"
-os.environ["XDG_CONFIG_HOME"] = "/tmp"
-os.environ["NUMBA_DISABLE_CACHE"] = "1"
-os.makedirs("/tmp/torch", exist_ok=True)
-os.makedirs("/tmp/huggingface", exist_ok=True)
-os.makedirs("/tmp/flagged", exist_ok=True)
-
-# Output folder
-output_dir = "/tmp/outputs"
-os.makedirs(output_dir, exist_ok=True)
 
-# Initialize tone converter
-ckpt_converter = "checkpoints/converter/config.json"
-tone_color_converter = ToneColorConverter(ckpt_converter)
+from TTS.api import TTS
+from openvoice import se_extractor, ToneColorConverter
 
-# Device setting
-device = "cuda" if torch.cuda.is_available() else "cpu"
+# Import your local english.py logic
+from meloTTS import english
+
+# Paths
+device = "cuda" if os.system("nvidia-smi") == 0 else "cpu"
+output_dir = "outputs"
+os.makedirs(output_dir, exist_ok=True)
+
+# Load OpenVoice tone converter
+tone_color_converter = ToneColorConverter(f"{os.getcwd()}/checkpoints", device=device)
+tone_color_converter.load_model()
 
 def clone_and_speak(text, speaker_wav):
     if not speaker_wav:
         return "Please upload a reference .wav file."
 
-    # import melo.text.english as english
-    # original_g2p = english.g2p
-
-    # def patched_g2p(text):
-    #     phones, tones, word2ph = original_g2p(text)
-    #     # Fix: wrap ints in list to avoid TypeError
-    #     word2ph_fixed = []
-    #     for item in word2ph:
-    #         if isinstance(item, int):
-    #             word2ph_fixed.append([item])
-    #         else:
-    #             word2ph_fixed.append(item)
-    #     return phones, tones, word2ph_fixed
-
-    # english.g2p = patched_g2p
-
     base_name = f"output_{int(time.time())}_{uuid.uuid4().hex[:6]}"
     tmp_melo_path = f"{output_dir}/{base_name}_tmp.wav"
     final_output_path = f"{output_dir}/{base_name}_converted.wav"
@@ -63,33 +129,32 @@ def clone_and_speak(text, speaker_wav):
     default_speaker_id = next(iter(speaker_ids.values()))
 
     # Generate base TTS voice
-    speed = 1.0
-    model.tts_to_file(text, default_speaker_id, tmp_melo_path,speed=speed)
+    model.tts_to_file(text, speaker_id=default_speaker_id, file_path=tmp_melo_path, speed=1.0)
 
-    # Use speaker_wav as reference to extract style embedding
-    from openvoice import se_extractor
+    # Extract style embedding
     ref_se, _ = se_extractor.get_se(speaker_wav, tone_color_converter, vad=False)
 
-    # Run the tone conversion
+    # Convert tone
     tone_color_converter.convert(
         audio_src_path=tmp_melo_path,
         src_se=ref_se,
         tgt_se=ref_se,
         output_path=final_output_path,
-        message="@HuggingFace",
+        message="@HuggingFace"
     )
 
     return final_output_path
 
-# Gradio interface
-gr.Interface(
+# Gradio Interface
+demo = gr.Interface(
     fn=clone_and_speak,
     inputs=[
-        gr.Textbox(label="Enter Text"),
-        gr.Audio(type="filepath", label="Upload a Reference Voice (.wav)")
+        gr.Textbox(label="Text to Synthesize"),
+        gr.Audio(label="Reference Voice (WAV)", type="filepath")
     ],
-    outputs=gr.Audio(label="Synthesized Output"),
-    flagging_dir="/tmp/flagged",
-    title="Text to Voice using Melo TTS + OpenVoice",
-    description="Use Melo TTS for base synthesis and OpenVoice to apply a reference speaker's tone.",
-).launch()
+    outputs=gr.Audio(label="Cloned Voice Output"),
+    title="Voice Cloner with MeloTTS + OpenVoice"
+)
+
+if __name__ == "__main__":
+    demo.launch()