init muxi

metaX-C500-f5-tts/F5-TTS/src/f5_tts/api.py

@@ -0,0 +1,164 @@
+import random
+import sys
+from importlib.resources import files
+
+import soundfile as sf
+import tqdm
+from cached_path import cached_path
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+
+from f5_tts.infer.utils_infer import (
+    infer_process,
+    load_model,
+    load_vocoder,
+    preprocess_ref_audio_text,
+    remove_silence_for_generated_wav,
+    save_spectrogram,
+    transcribe,
+)
+from f5_tts.model.utils import seed_everything
+
+
+class F5TTS:
+    def __init__(
+        self,
+        model="F5TTS_v1_Base",
+        ckpt_file="",
+        vocab_file="",
+        ode_method="euler",
+        use_ema=True,
+        vocoder_local_path=None,
+        device=None,
+        hf_cache_dir=None,
+    ):
+        model_cfg = OmegaConf.load(str(files("f5_tts").joinpath(f"configs/{model}.yaml")))
+        model_cls = get_class(f"f5_tts.model.{model_cfg.model.backbone}")
+        model_arc = model_cfg.model.arch
+
+        self.mel_spec_type = model_cfg.model.mel_spec.mel_spec_type
+        self.target_sample_rate = model_cfg.model.mel_spec.target_sample_rate
+
+        self.ode_method = ode_method
+        self.use_ema = use_ema
+
+        if device is not None:
+            self.device = device
+        else:
+            import torch
+
+            self.device = (
+                "cuda"
+                if torch.cuda.is_available()
+                else "xpu"
+                if torch.xpu.is_available()
+                else "mps"
+                if torch.backends.mps.is_available()
+                else "cpu"
+            )
+
+        # Load models
+        self.vocoder = load_vocoder(
+            self.mel_spec_type, vocoder_local_path is not None, vocoder_local_path, self.device, hf_cache_dir
+        )
+
+        repo_name, ckpt_step, ckpt_type = "F5-TTS", 1250000, "safetensors"
+
+        # override for previous models
+        if model == "F5TTS_Base":
+            if self.mel_spec_type == "vocos":
+                ckpt_step = 1200000
+            elif self.mel_spec_type == "bigvgan":
+                model = "F5TTS_Base_bigvgan"
+                ckpt_type = "pt"
+        elif model == "E2TTS_Base":
+            repo_name = "E2-TTS"
+            ckpt_step = 1200000
+
+        if not ckpt_file:
+            ckpt_file = str(
+                cached_path(f"hf://SWivid/{repo_name}/{model}/model_{ckpt_step}.{ckpt_type}", cache_dir=hf_cache_dir)
+            )
+        self.ema_model = load_model(
+            model_cls, model_arc, ckpt_file, self.mel_spec_type, vocab_file, self.ode_method, self.use_ema, self.device
+        )
+
+    def transcribe(self, ref_audio, language=None):
+        return transcribe(ref_audio, language)
+
+    def export_wav(self, wav, file_wave, remove_silence=False):
+        sf.write(file_wave, wav, self.target_sample_rate)
+
+        if remove_silence:
+            remove_silence_for_generated_wav(file_wave)
+
+    def export_spectrogram(self, spec, file_spec):
+        save_spectrogram(spec, file_spec)
+
+    def infer(
+        self,
+        ref_file,
+        ref_text,
+        gen_text,
+        show_info=print,
+        progress=tqdm,
+        target_rms=0.1,
+        cross_fade_duration=0.15,
+        sway_sampling_coef=-1,
+        cfg_strength=2,
+        nfe_step=32,
+        speed=1.0,
+        fix_duration=None,
+        remove_silence=False,
+        file_wave=None,
+        file_spec=None,
+        seed=None,
+    ):
+        if seed is None:
+            seed = random.randint(0, sys.maxsize)
+        seed_everything(seed)
+        self.seed = seed
+
+        ref_file, ref_text = preprocess_ref_audio_text(ref_file, ref_text)
+
+        wav, sr, spec = infer_process(
+            ref_file,
+            ref_text,
+            gen_text,
+            self.ema_model,
+            self.vocoder,
+            self.mel_spec_type,
+            show_info=show_info,
+            progress=progress,
+            target_rms=target_rms,
+            cross_fade_duration=cross_fade_duration,
+            nfe_step=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+            speed=speed,
+            fix_duration=fix_duration,
+            device=self.device,
+        )
+
+        if file_wave is not None:
+            self.export_wav(wav, file_wave, remove_silence)
+
+        if file_spec is not None:
+            self.export_spectrogram(spec, file_spec)
+
+        return wav, sr, spec
+
+
+if __name__ == "__main__":
+    f5tts = F5TTS()
+
+    wav, sr, spec = f5tts.infer(
+        ref_file=str(files("f5_tts").joinpath("infer/examples/basic/basic_ref_en.wav")),
+        ref_text="some call me nature, others call me mother nature.",
+        gen_text="""I don't really care what you call me. I've been a silent spectator, watching species evolve, empires rise and fall. But always remember, I am mighty and enduring. Respect me and I'll nurture you; ignore me and you shall face the consequences.""",
+        file_wave=str(files("f5_tts").joinpath("../../tests/api_out.wav")),
+        file_spec=str(files("f5_tts").joinpath("../../tests/api_out.png")),
+        seed=None,
+    )
+
+    print("seed :", f5tts.seed)