Update voice_processing.py

voice_processing.py

@@ -1,248 +1,248 @@
-import asyncio
-import datetime
-import logging
-import os
-import time
-import traceback
-import tempfile
-
-import edge_tts
-import librosa
-import torch
-from fairseq import checkpoint_utils
-import uuid
-
-from config import Config
-from lib.infer_pack.models import (
-    SynthesizerTrnMs256NSFsid,
-    SynthesizerTrnMs256NSFsid_nono,
-    SynthesizerTrnMs768NSFsid,
-    SynthesizerTrnMs768NSFsid_nono,
-)
-from rmvpe import RMVPE
-from vc_infer_pipeline import VC
-
-# Set logging levels
-logging.getLogger("fairseq").setLevel(logging.WARNING)
-logging.getLogger("numba").setLevel(logging.WARNING)
-logging.getLogger("markdown_it").setLevel(logging.WARNING)
-logging.getLogger("urllib3").setLevel(logging.WARNING)
-logging.getLogger("matplotlib").setLevel(logging.WARNING)
-
-limitation = os.getenv("SYSTEM") == "spaces"
-
-config = Config()
-
-# Edge TTS
-tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
-tts_voices = ["mn-MN-BataaNeural", "mn-MN-YesuiNeural"]  # Specific voices
-
-# RVC models
-model_root = "weights"
-models = [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
-models.sort()
-
-def get_unique_filename(extension):
-    return f"{uuid.uuid4()}.{extension}"
-
-
-#edge_output_filename = get_unique_filename("mp3")
-
-
-def model_data(model_name):
-    # global n_spk, tgt_sr, net_g, vc, cpt, version, index_file
-    pth_path = [
-        f"{model_root}/{model_name}/{f}"
-        for f in os.listdir(f"{model_root}/{model_name}")
-        if f.endswith(".pth")
-    ][0]
-    print(f"Loading {pth_path}")
-    cpt = torch.load(pth_path, map_location="cpu")
-    tgt_sr = cpt["config"][-1]
-    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
-    if_f0 = cpt.get("f0", 1)
-    version = cpt.get("version", "v1")
-    if version == "v1":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
-        else:
-            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-    elif version == "v2":
-        if if_f0 == 1:
-            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
-        else:
-            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
-    else:
-        raise ValueError("Unknown version")
-    del net_g.enc_q
-    net_g.load_state_dict(cpt["weight"], strict=False)
-    print("Model loaded")
-    net_g.eval().to(config.device)
-    if config.is_half:
-        net_g = net_g.half()
-    else:
-        net_g = net_g.float()
-    vc = VC(tgt_sr, config)
-    # n_spk = cpt["config"][-3]
-
-    index_files = [
-        f"{model_root}/{model_name}/{f}"
-        for f in os.listdir(f"{model_root}/{model_name}")
-        if f.endswith(".index")
-    ]
-    if len(index_files) == 0:
-        print("No index file found")
-        index_file = ""
-    else:
-        index_file = index_files[0]
-        print(f"Index file found: {index_file}")
-
-    return tgt_sr, net_g, vc, version, index_file, if_f0
-
-
-def load_hubert():
-    # global hubert_model
-    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
-        ["hubert_base.pt"],
-        suffix="",
-    )
-    hubert_model = models[0]
-    hubert_model = hubert_model.to(config.device)
-    if config.is_half:
-        hubert_model = hubert_model.half()
-    else:
-        hubert_model = hubert_model.float()
-    return hubert_model.eval()
-
-def get_model_names():
-    model_root = "weights"  # Assuming this is where your models are stored
-    return [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
-
-async def tts(
-    model_name,
-    tts_text,
-    tts_voice,
-    index_rate,
-    use_uploaded_voice,
-    uploaded_voice,
-):
-    # Default values for parameters used in EdgeTTS
-    speed = 0  # Default speech speed
-    f0_up_key = 0  # Default pitch adjustment
-    f0_method = "rmvpe"  # Default pitch extraction method
-    protect = 0.33  # Default protect value
-    filter_radius = 3
-    resample_sr = 0
-    rms_mix_rate = 0.25
-    edge_time = 0  # Initialize edge_time
-
-    edge_output_filename = get_unique_filename("mp3")
-
-
-    try:
-        if use_uploaded_voice:
-            if uploaded_voice is None:
-                return "No voice file uploaded.", None, None
-            
-            # Process the uploaded voice file
-            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
-                tmp_file.write(uploaded_voice)
-                uploaded_file_path = tmp_file.name
-
-            #uploaded_file_path = uploaded_voice.name
-            audio, sr = librosa.load(uploaded_file_path, sr=16000, mono=True)
-        else:
-            # EdgeTTS processing
-            if limitation and len(tts_text) > 4000:
-                return (
-                    f"Text characters should be at most 280 in this huggingface space, but got {len(tts_text)} characters.",
-                    None,
-                    None,
-                )
-            
-            # Invoke Edge TTS
-            t0 = time.time()
-            speed_str = f"+{speed}%" if speed >= 0 else f"{speed}%"
-            await edge_tts.Communicate(
-                tts_text, tts_voice, rate=speed_str
-            ).save(edge_output_filename)
-            t1 = time.time()
-            edge_time = t1 - t0
-
-            audio, sr = librosa.load(edge_output_filename, sr=16000, mono=True)
-
-        # Common processing after loading the audio
-        duration = len(audio) / sr
-        print(f"Audio duration: {duration}s")
-        if limitation and duration >= 20:
-            return (
-                f"Audio should be less than 20 seconds in this huggingface space, but got {duration}s.",
-                None,
-                None,
-            )
-
-        f0_up_key = int(f0_up_key)
-        tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name)
-
-        # Setup for RMVPE or other pitch extraction methods
-        if f0_method == "rmvpe":
-            vc.model_rmvpe = rmvpe_model
-
-        # Perform voice conversion pipeline
-        times = [0, 0, 0]
-        audio_opt = vc.pipeline(
-            hubert_model,
-            net_g,
-            0,
-            audio,
-            edge_output_filename if not use_uploaded_voice else uploaded_file_path,
-            times,
-            f0_up_key,
-            f0_method,
-            index_file,
-            index_rate,
-            if_f0,
-            filter_radius,
-            tgt_sr,
-            resample_sr,
-            rms_mix_rate,
-            version,
-            protect,
-            None,
-        )
-
-        if tgt_sr != resample_sr and resample_sr >= 16000:
-            tgt_sr = resample_sr
-        
-        info = f"Success. Time: tts: {edge_time}s, npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s"
-        print(info)
-        return (
-            info,
-            edge_output_filename if not use_uploaded_voice else None,   
-            (tgt_sr, audio_opt),
-            edge_output_filename  
-        )
-
-    except EOFError:
-        info = (
-            "output not valid. This may occur when input text and speaker do not match."
-        )
-        print(info)
-        return info, None, None
-    except Exception as e:
-        traceback_info = traceback.format_exc()
-        print(traceback_info)
-        return str(e), None, None
-
-
-voice_mapping = {
-    "Mongolian Male": "mn-MN-BataaNeural",
-    "Mongolian Female": "mn-MN-YesuiNeural"
-}
-
-
-
-hubert_model = load_hubert()
-
-rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device)
-
+import asyncio
+import datetime
+import logging
+import os
+import time
+import traceback
+import tempfile
+
+import edge_tts
+import librosa
+import torch
+from fairseq import checkpoint_utils
+import uuid
+
+from config import Config
+from lib.infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
+from rmvpe import RMVPE
+from vc_infer_pipeline import VC
+
+# Set logging levels
+logging.getLogger("fairseq").setLevel(logging.WARNING)
+logging.getLogger("numba").setLevel(logging.WARNING)
+logging.getLogger("markdown_it").setLevel(logging.WARNING)
+logging.getLogger("urllib3").setLevel(logging.WARNING)
+logging.getLogger("matplotlib").setLevel(logging.WARNING)
+
+limitation = os.getenv("SYSTEM") == "spaces"
+
+config = Config()
+
+# Edge TTS
+tts_voice_list = asyncio.get_event_loop().run_until_complete(edge_tts.list_voices())
+tts_voices = ["mn-MN-BataaNeural", "mn-MN-YesuiNeural"]  # Specific voices
+
+# RVC models
+model_root = "weights"
+models = [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
+models.sort()
+
+def get_unique_filename(extension):
+    return f"{uuid.uuid4()}.{extension}"
+
+
+#edge_output_filename = get_unique_filename("mp3")
+
+
+def model_data(model_name):
+    # global n_spk, tgt_sr, net_g, vc, cpt, version, index_file
+    pth_path = [
+        f"{model_root}/{model_name}/{f}"
+        for f in os.listdir(f"{model_root}/{model_name}")
+        if f.endswith(".pth")
+    ][0]
+    print(f"Loading {pth_path}")
+    cpt = torch.load(pth_path, map_location="cpu")
+    tgt_sr = cpt["config"][-1]
+    cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
+    if_f0 = cpt.get("f0", 1)
+    version = cpt.get("version", "v1")
+    if version == "v1":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
+    elif version == "v2":
+        if if_f0 == 1:
+            net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+        else:
+            net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
+    else:
+        raise ValueError("Unknown version")
+    del net_g.enc_q
+    net_g.load_state_dict(cpt["weight"], strict=False)
+    print("Model loaded")
+    net_g.eval().to(config.device)
+    if config.is_half:
+        net_g = net_g.half()
+    else:
+        net_g = net_g.float()
+    vc = VC(tgt_sr, config)
+    # n_spk = cpt["config"][-3]
+
+    index_files = [
+        f"{model_root}/{model_name}/{f}"
+        for f in os.listdir(f"{model_root}/{model_name}")
+        if f.endswith(".index")
+    ]
+    if len(index_files) == 0:
+        print("No index file found")
+        index_file = ""
+    else:
+        index_file = index_files[0]
+        print(f"Index file found: {index_file}")
+
+    return tgt_sr, net_g, vc, version, index_file, if_f0
+
+
+def load_hubert():
+    # global hubert_model
+    models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
+        ["hubert_base.pt"],
+        suffix="",
+    )
+    hubert_model = models[0]
+    hubert_model = hubert_model.to(config.device)
+    if config.is_half:
+        hubert_model = hubert_model.half()
+    else:
+        hubert_model = hubert_model.float()
+    return hubert_model.eval()
+
+def get_model_names():
+    model_root = "weights"  # Assuming this is where your models are stored
+    return [d for d in os.listdir(model_root) if os.path.isdir(f"{model_root}/{d}")]
+
+async def tts(
+    model_name,
+    tts_text,
+    tts_voice,
+    index_rate,
+    use_uploaded_voice,
+    uploaded_voice,
+):
+    # Default values for parameters used in EdgeTTS
+    speed = 0  # Default speech speed
+    f0_up_key = 0  # Default pitch adjustment
+    f0_method = "rmvpe"  # Default pitch extraction method
+    protect = 0.33  # Default protect value
+    filter_radius = 3
+    resample_sr = 0
+    rms_mix_rate = 0.25
+    edge_time = 0  # Initialize edge_time
+
+    edge_output_filename = get_unique_filename("mp3")
+
+
+    try:
+        if use_uploaded_voice:
+            if uploaded_voice is None:
+                return "No voice file uploaded.", None, None
+            
+            # Process the uploaded voice file
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
+                tmp_file.write(uploaded_voice)
+                uploaded_file_path = tmp_file.name
+
+            #uploaded_file_path = uploaded_voice.name
+            audio, sr = librosa.load(uploaded_file_path, sr=16000, mono=True)
+        else:
+            # EdgeTTS processing
+            if limitation and len(tts_text) > 12000:
+                return (
+                    f"Text characters should be at most 280 in this huggingface space, but got {len(tts_text)} characters.",
+                    None,
+                    None,
+                )
+            
+            # Invoke Edge TTS
+            t0 = time.time()
+            speed_str = f"+{speed}%" if speed >= 0 else f"{speed}%"
+            await edge_tts.Communicate(
+                tts_text, tts_voice, rate=speed_str
+            ).save(edge_output_filename)
+            t1 = time.time()
+            edge_time = t1 - t0
+
+            audio, sr = librosa.load(edge_output_filename, sr=16000, mono=True)
+
+        # Common processing after loading the audio
+        duration = len(audio) / sr
+        print(f"Audio duration: {duration}s")
+        if limitation and duration >= 20000:
+            return (
+                f"Audio should be less than 20 seconds in this huggingface space, but got {duration}s.",
+                None,
+                None,
+            )
+
+        f0_up_key = int(f0_up_key)
+        tgt_sr, net_g, vc, version, index_file, if_f0 = model_data(model_name)
+
+        # Setup for RMVPE or other pitch extraction methods
+        if f0_method == "rmvpe":
+            vc.model_rmvpe = rmvpe_model
+
+        # Perform voice conversion pipeline
+        times = [0, 0, 0]
+        audio_opt = vc.pipeline(
+            hubert_model,
+            net_g,
+            0,
+            audio,
+            edge_output_filename if not use_uploaded_voice else uploaded_file_path,
+            times,
+            f0_up_key,
+            f0_method,
+            index_file,
+            index_rate,
+            if_f0,
+            filter_radius,
+            tgt_sr,
+            resample_sr,
+            rms_mix_rate,
+            version,
+            protect,
+            None,
+        )
+
+        if tgt_sr != resample_sr and resample_sr >= 16000:
+            tgt_sr = resample_sr
+        
+        info = f"Success. Time: tts: {edge_time}s, npy: {times[0]}s, f0: {times[1]}s, infer: {times[2]}s"
+        print(info)
+        return (
+            info,
+            edge_output_filename if not use_uploaded_voice else None,   
+            (tgt_sr, audio_opt),
+            edge_output_filename  
+        )
+
+    except EOFError:
+        info = (
+            "output not valid. This may occur when input text and speaker do not match."
+        )
+        print(info)
+        return info, None, None
+    except Exception as e:
+        traceback_info = traceback.format_exc()
+        print(traceback_info)
+        return str(e), None, None
+
+
+voice_mapping = {
+    "Mongolian Male": "mn-MN-BataaNeural",
+    "Mongolian Female": "mn-MN-YesuiNeural"
+}
+
+
+
+hubert_model = load_hubert()
+
+rmvpe_model = RMVPE("rmvpe.pt", config.is_half, config.device)
+