Delete lib/infer.py

lib/infer.py

@@ -1,217 +0,0 @@
-import os
-import torch
-from multiprocessing import cpu_count
-from lib.modules import VC
-from lib.split_audio import split_silence_nonsilent, adjust_audio_lengths, combine_silence_nonsilent
-
-class Configs:
-    def __init__(self, device, is_half):
-        self.device = device
-        self.is_half = is_half
-        self.n_cpu = 0
-        self.gpu_name = None
-        self.gpu_mem = None
-        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
-
-    def device_config(self) -> tuple:
-        if torch.cuda.is_available():
-            i_device = int(self.device.split(":")[-1])
-            self.gpu_name = torch.cuda.get_device_name(i_device)
-            #if (
-#                    ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
-#                    or "P40" in self.gpu_name.upper()
-#                    or "1060" in self.gpu_name
-#                    or "1070" in self.gpu_name
-#                    or "1080" in self.gpu_name
-#            ):
-#                print("16 series/10 series P40 forced single precision")
-#                self.is_half = False
-#                for config_file in ["32k.json", "40k.json", "48k.json"]:
-#                    with open(BASE_DIR / "src" / "configs" / config_file, "r") as f:
-#                        strr = f.read().replace("true", "false")
-#                    with open(BASE_DIR / "src" / "configs" / config_file, "w") as f:
-#                        f.write(strr)
-#                with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f:
-#                    strr = f.read().replace("3.7", "3.0")
-#                with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f:
-#                    f.write(strr)
-#            else:
-#                self.gpu_name = None
-#            self.gpu_mem = int(
-#                torch.cuda.get_device_properties(i_device).total_memory
-#                / 1024
-#                / 1024
-#                / 1024
-#                + 0.4
-#            )
-#            if self.gpu_mem <= 4:
-#                with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "r") as f:
-#                    strr = f.read().replace("3.7", "3.0")
-#                with open(BASE_DIR / "src" / "trainset_preprocess_pipeline_print.py", "w") as f:
-#                    f.write(strr)
-        elif torch.backends.mps.is_available():
-            print("No supported N-card found, use MPS for inference")
-            self.device = "mps"
-        else:
-            print("No supported N-card found, use CPU for inference")
-            self.device = "cpu"
-            self.is_half = True
-
-        if self.n_cpu == 0:
-            self.n_cpu = cpu_count()
-
-        if self.is_half:
-            # 6G memory config
-            x_pad = 3
-            x_query = 10
-            x_center = 60
-            x_max = 65
-        else:
-            # 5G memory config
-            x_pad = 1
-            x_query = 6
-            x_center = 38
-            x_max = 41
-
-        if self.gpu_mem != None and self.gpu_mem <= 4:
-            x_pad = 1
-            x_query = 5
-            x_center = 30
-            x_max = 32
-
-        return x_pad, x_query, x_center, x_max
-
-def get_model(voice_model):
-    model_dir = os.path.join(os.getcwd(), "models", voice_model)
-    model_filename, index_filename = None, None
-    for file in os.listdir(model_dir):
-        ext = os.path.splitext(file)[1]
-        if ext == '.pth':
-            model_filename = file
-        if ext == '.index':
-            index_filename = file
-
-    if model_filename is None:
-        print(f'No model file exists in {models_dir}.')
-        return None, None
-
-    return os.path.join(model_dir, model_filename), os.path.join(model_dir, index_filename) if index_filename else ''
-
-def infer_audio(
-    model_name,
-    audio_path,
-    f0_change=0,
-    f0_method="rmvpe+",
-    min_pitch="50",
-    max_pitch="1100",
-    crepe_hop_length=128,
-    index_rate=0.75,
-    filter_radius=3,
-    rms_mix_rate=0.25,
-    protect=0.33,
-    split_infer=False,
-    min_silence=500,
-    silence_threshold=-50,
-    seek_step=1,
-    keep_silence=100,
-    do_formant=False,
-    quefrency=0,
-    timbre=1,
-    f0_autotune=False,
-    audio_format="wav",
-    resample_sr=0,
-    hubert_model_path="assets/hubert/hubert_base.pt",
-    rmvpe_model_path="assets/rmvpe/rmvpe.pt",
-    fcpe_model_path="assets/fcpe/fcpe.pt"
-    ):
-    os.environ["rmvpe_model_path"] = rmvpe_model_path
-    os.environ["fcpe_model_path"] = fcpe_model_path
-    configs = Configs('cuda:0', True)
-    vc = VC(configs)
-    pth_path, index_path = get_model(model_name)
-    vc_data = vc.get_vc(pth_path, protect, 0.5)
-    
-    if split_infer:
-        inferred_files = []
-        temp_dir = os.path.join(main_dir, "seperate", "temp")
-        os.makedirs(temp_dir, exist_ok=True)
-        print("Splitting audio to silence and nonsilent segments.")
-        silence_files, nonsilent_files = split_silence_nonsilent(audio_path, min_silence, silence_threshold, seek_step, keep_silence)
-        print(f"Total silence segments: {len(silence_files)}.\nTotal nonsilent segments: {len(nonsilent_files)}.")
-        for i, nonsilent_file in enumerate(nonsilent_files):
-            print(f"Inferring nonsilent audio {i+1}")
-            inference_info, audio_data, output_path = vc.vc_single(
-            0,
-            audio_path,
-            f0_change,
-            f0_method,
-            index_path,
-            index_path,
-            index_rate,
-            filter_radius,
-            resample_sr,
-            rms_mix_rate,
-            protect,
-            audio_format,
-            crepe_hop_length,
-            do_formant,
-            quefrency,
-            timbre,
-            min_pitch,
-            max_pitch,
-            f0_autotune,
-            hubert_model_path
-            )
-            if inference_info[0] == "Success.":
-                print("Inference ran successfully.")
-                print(inference_info[1])
-                print("Times:\nnpy: %.2fs f0: %.2fs infer: %.2fs\nTotal time: %.2fs" % (*inference_info[2],))
-            else:
-                print(f"An error occurred while processing.\n{inference_info[0]}")
-                return None
-            shutil.move(output_path, temp_dir)
-            inferred_files.append(os.path.join(temp_dir, os.path.basename(output_path)))
-        print("Adjusting inferred audio lengths.")
-        adjusted_inferred_files = adjust_audio_lengths(nonsilent_files, inferred_files)
-        print("Combining silence and inferred audios.")
-        output_count = 1
-        while True:
-            output_path = os.path.join(os.getcwd(), "output", f"{os.path.splitext(os.path.basename(audio_path))[0]}{model_name}{f0_method.capitalize()}_{output_count}.{audio_format}")
-            if not os.path.exists(output_path):
-                break
-            output_count += 1
-        inferred_audio = combine_silence_nonsilent(silence_files, adjusted_inferred_files, keep_silence, output_path)
-        shutil.rmtree(os.path.join(main_dir, "seperate", "temp"))
-    else:
-        inference_info, audio_data, output_path = vc.vc_single(
-            0,
-            audio_path,
-            f0_change,
-            f0_method,
-            index_path,
-            index_path,
-            index_rate,
-            filter_radius,
-            resample_sr,
-            rms_mix_rate,
-            protect,
-            audio_format,
-            crepe_hop_length,
-            do_formant,
-            quefrency,
-            timbre,
-            min_pitch,
-            max_pitch,
-            f0_autotune
-        )
-        if inference_info[0] == "Success.":
-            print("Inference ran successfully.")
-            print(inference_info[1])
-            print("Times:\nnpy: %.2fs f0: %.2fs infer: %.2fs\nTotal time: %.2fs" % (*inference_info[2],))
-        else:
-            print(f"An error occurred while processing.\n{inference_info[0]}")
-            return None
-    
-    del configs, vc
-    gc.collect()
-    return output_path