Update app.py

app.py

@@ -1,125 +1,60 @@
-# 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 time
 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 TTS.api import TTS
-from openvoice import se_extractor
 from openvoice.api import ToneColorConverter
-
-# Import your local english.py logic
 from meloTTS import english
 
-# Paths
-device = "cuda" if os.system("nvidia-smi") == 0 else "cpu"
-output_dir = "outputs"
+# 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)
 
-# Load OpenVoice tone converter
-tone_color_converter = ToneColorConverter(f"{os.getcwd()}/checkpoints", device=device)
-tone_color_converter.load_model()
+# 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"
@@ -130,32 +65,99 @@ def clone_and_speak(text, speaker_wav):
     default_speaker_id = next(iter(speaker_ids.values()))
 
     # Generate base TTS voice
-    model.tts_to_file(text, speaker_id=default_speaker_id, file_path=tmp_melo_path, speed=1.0)
+    speed = 1.0
+    model.tts_to_file(text, default_speaker_id, tmp_melo_path,speed=speed)
 
-    # Extract style embedding
+    # 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)
 
-    # Convert tone
+    # 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"
+        message="@HuggingFace",
     )
 
     return final_output_path
 
-# Gradio Interface
-demo = gr.Interface(
+# Gradio interface
+gr.Interface(
     fn=clone_and_speak,
     inputs=[
-        gr.Textbox(label="Text to Synthesize"),
-        gr.Audio(label="Reference Voice (WAV)", type="filepath")
+        gr.Textbox(label="Enter Text"),
+        gr.Audio(type="filepath", label="Upload a Reference Voice (.wav)")
     ],
-    outputs=gr.Audio(label="Cloned Voice Output"),
-    title="Voice Cloner with MeloTTS + OpenVoice"
-)
+    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 time
+# import uuid
+# import gradio as gr
+
+# from TTS.api import TTS
+# from openvoice import se_extractor
+# from openvoice.api import ToneColorConverter
+
+# # 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."
+
+#     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
+#     model.tts_to_file(text, speaker_id=default_speaker_id, file_path=tmp_melo_path, speed=1.0)
+
+#     # Extract style embedding
+#     ref_se, _ = se_extractor.get_se(speaker_wav, tone_color_converter, vad=False)
+
+#     # 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"
+#     )
+
+#     return final_output_path
+
+# # Gradio Interface
+# demo = gr.Interface(
+#     fn=clone_and_speak,
+#     inputs=[
+#         gr.Textbox(label="Text to Synthesize"),
+#         gr.Audio(label="Reference Voice (WAV)", type="filepath")
+#     ],
+#     outputs=gr.Audio(label="Cloned Voice Output"),
+#     title="Voice Cloner with MeloTTS + OpenVoice"
+# )
 
-if __name__ == "__main__":
-    demo.launch()
+# if __name__ == "__main__":
+#     demo.launch()