init ascend tts

2025-09-05 11:27:43 +08:00
parent d53ac91bb6
commit b92a65b0fa
602 changed files with 590901 additions and 1 deletions
--- a/ascend_910-f5-tts/F5-TTS/src/f5_tts/infer/infer_cli.py
+++ b/ascend_910-f5-tts/F5-TTS/src/f5_tts/infer/infer_cli.py
@@ -0,0 +1,368 @@
+import argparse
+import codecs
+import os
+import re
+from datetime import datetime
+from importlib.resources import files
+from pathlib import Path
+
+import numpy as np
+import soundfile as sf
+import tomli
+from cached_path import cached_path
+from hydra.utils import get_class
+from omegaconf import OmegaConf
+
+from f5_tts.infer.utils_infer import (
+    cfg_strength,
+    cross_fade_duration,
+    device,
+    fix_duration,
+    infer_process,
+    load_model,
+    load_vocoder,
+    mel_spec_type,
+    nfe_step,
+    preprocess_ref_audio_text,
+    remove_silence_for_generated_wav,
+    speed,
+    sway_sampling_coef,
+    target_rms,
+)
+
+
+parser = argparse.ArgumentParser(
+    prog="python3 infer-cli.py",
+    description="Commandline interface for E2/F5 TTS with Advanced Batch Processing.",
+    epilog="Specify options above to override one or more settings from config.",
+)
+parser.add_argument(
+    "-c",
+    "--config",
+    type=str,
+    default=os.path.join(files("f5_tts").joinpath("infer/examples/basic"), "basic.toml"),
+    help="The configuration file, default see infer/examples/basic/basic.toml",
+)
+
+
+# Note. Not to provide default value here in order to read default from config file
+
+parser.add_argument(
+    "-m",
+    "--model",
+    type=str,
+    help="The model name: F5TTS_v1_Base | F5TTS_Base | E2TTS_Base | etc.",
+)
+parser.add_argument(
+    "-mc",
+    "--model_cfg",
+    type=str,
+    help="The path to F5-TTS model config file .yaml",
+)
+parser.add_argument(
+    "-p",
+    "--ckpt_file",
+    type=str,
+    help="The path to model checkpoint .pt, leave blank to use default",
+)
+parser.add_argument(
+    "-v",
+    "--vocab_file",
+    type=str,
+    help="The path to vocab file .txt, leave blank to use default",
+)
+parser.add_argument(
+    "-r",
+    "--ref_audio",
+    type=str,
+    help="The reference audio file.",
+)
+parser.add_argument(
+    "-s",
+    "--ref_text",
+    type=str,
+    help="The transcript/subtitle for the reference audio",
+)
+parser.add_argument(
+    "-t",
+    "--gen_text",
+    type=str,
+    help="The text to make model synthesize a speech",
+)
+parser.add_argument(
+    "-f",
+    "--gen_file",
+    type=str,
+    help="The file with text to generate, will ignore --gen_text",
+)
+parser.add_argument(
+    "-o",
+    "--output_dir",
+    type=str,
+    help="The path to output folder",
+)
+parser.add_argument(
+    "-w",
+    "--output_file",
+    type=str,
+    help="The name of output file",
+)
+parser.add_argument(
+    "--save_chunk",
+    action="store_true",
+    help="To save each audio chunks during inference",
+)
+parser.add_argument(
+    "--remove_silence",
+    action="store_true",
+    help="To remove long silence found in ouput",
+)
+parser.add_argument(
+    "--load_vocoder_from_local",
+    action="store_true",
+    help="To load vocoder from local dir, default to ../checkpoints/vocos-mel-24khz",
+)
+parser.add_argument(
+    "--vocoder_name",
+    type=str,
+    choices=["vocos", "bigvgan"],
+    help=f"Used vocoder name: vocos | bigvgan, default {mel_spec_type}",
+)
+parser.add_argument(
+    "--target_rms",
+    type=float,
+    help=f"Target output speech loudness normalization value, default {target_rms}",
+)
+parser.add_argument(
+    "--cross_fade_duration",
+    type=float,
+    help=f"Duration of cross-fade between audio segments in seconds, default {cross_fade_duration}",
+)
+parser.add_argument(
+    "--nfe_step",
+    type=int,
+    help=f"The number of function evaluation (denoising steps), default {nfe_step}",
+)
+parser.add_argument(
+    "--cfg_strength",
+    type=float,
+    help=f"Classifier-free guidance strength, default {cfg_strength}",
+)
+parser.add_argument(
+    "--sway_sampling_coef",
+    type=float,
+    help=f"Sway Sampling coefficient, default {sway_sampling_coef}",
+)
+parser.add_argument(
+    "--speed",
+    type=float,
+    help=f"The speed of the generated audio, default {speed}",
+)
+parser.add_argument(
+    "--fix_duration",
+    type=float,
+    help=f"Fix the total duration (ref and gen audios) in seconds, default {fix_duration}",
+)
+parser.add_argument(
+    "--device",
+    type=str,
+    help="Specify the device to run on",
+)
+args = parser.parse_args()
+
+
+# config file
+
+config = tomli.load(open(args.config, "rb"))
+
+
+# command-line interface parameters
+
+model = args.model or config.get("model", "F5TTS_v1_Base")
+ckpt_file = args.ckpt_file or config.get("ckpt_file", "")
+vocab_file = args.vocab_file or config.get("vocab_file", "")
+
+ref_audio = args.ref_audio or config.get("ref_audio", "infer/examples/basic/basic_ref_en.wav")
+ref_text = (
+    args.ref_text
+    if args.ref_text is not None
+    else config.get("ref_text", "Some call me nature, others call me mother nature.")
+)
+gen_text = args.gen_text or config.get("gen_text", "Here we generate something just for test.")
+gen_file = args.gen_file or config.get("gen_file", "")
+
+output_dir = args.output_dir or config.get("output_dir", "tests")
+output_file = args.output_file or config.get(
+    "output_file", f"infer_cli_{datetime.now().strftime(r'%Y%m%d_%H%M%S')}.wav"
+)
+
+save_chunk = args.save_chunk or config.get("save_chunk", False)
+remove_silence = args.remove_silence or config.get("remove_silence", False)
+load_vocoder_from_local = args.load_vocoder_from_local or config.get("load_vocoder_from_local", False)
+
+vocoder_name = args.vocoder_name or config.get("vocoder_name", mel_spec_type)
+target_rms = args.target_rms or config.get("target_rms", target_rms)
+cross_fade_duration = args.cross_fade_duration or config.get("cross_fade_duration", cross_fade_duration)
+nfe_step = args.nfe_step or config.get("nfe_step", nfe_step)
+cfg_strength = args.cfg_strength or config.get("cfg_strength", cfg_strength)
+sway_sampling_coef = args.sway_sampling_coef or config.get("sway_sampling_coef", sway_sampling_coef)
+speed = args.speed or config.get("speed", speed)
+fix_duration = args.fix_duration or config.get("fix_duration", fix_duration)
+device = args.device or config.get("device", device)
+
+
+# patches for pip pkg user
+if "infer/examples/" in ref_audio:
+    ref_audio = str(files("f5_tts").joinpath(f"{ref_audio}"))
+if "infer/examples/" in gen_file:
+    gen_file = str(files("f5_tts").joinpath(f"{gen_file}"))
+if "voices" in config:
+    for voice in config["voices"]:
+        voice_ref_audio = config["voices"][voice]["ref_audio"]
+        if "infer/examples/" in voice_ref_audio:
+            config["voices"][voice]["ref_audio"] = str(files("f5_tts").joinpath(f"{voice_ref_audio}"))
+
+
+# ignore gen_text if gen_file provided
+
+if gen_file:
+    gen_text = codecs.open(gen_file, "r", "utf-8").read()
+
+
+# output path
+
+wave_path = Path(output_dir) / output_file
+# spectrogram_path = Path(output_dir) / "infer_cli_out.png"
+if save_chunk:
+    output_chunk_dir = os.path.join(output_dir, f"{Path(output_file).stem}_chunks")
+    if not os.path.exists(output_chunk_dir):
+        os.makedirs(output_chunk_dir)
+
+
+# load vocoder
+
+if vocoder_name == "vocos":
+    vocoder_local_path = "../checkpoints/vocos-mel-24khz"
+elif vocoder_name == "bigvgan":
+    vocoder_local_path = "../checkpoints/bigvgan_v2_24khz_100band_256x"
+
+vocoder = load_vocoder(
+    vocoder_name=vocoder_name, is_local=load_vocoder_from_local, local_path=vocoder_local_path, device=device
+)
+
+
+# load TTS model
+
+model_cfg = OmegaConf.load(
+    args.model_cfg or config.get("model_cfg", 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
+
+repo_name, ckpt_step, ckpt_type = "F5-TTS", 1250000, "safetensors"
+
+if model != "F5TTS_Base":
+    assert vocoder_name == model_cfg.model.mel_spec.mel_spec_type
+
+# override for previous models
+if model == "F5TTS_Base":
+    if vocoder_name == "vocos":
+        ckpt_step = 1200000
+    elif vocoder_name == "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}"))
+
+print(f"Using {model}...")
+ema_model = load_model(
+    model_cls, model_arc, ckpt_file, mel_spec_type=vocoder_name, vocab_file=vocab_file, device=device
+)
+
+
+# inference process
+
+
+def main():
+    main_voice = {"ref_audio": ref_audio, "ref_text": ref_text}
+    if "voices" not in config:
+        voices = {"main": main_voice}
+    else:
+        voices = config["voices"]
+        voices["main"] = main_voice
+    for voice in voices:
+        print("Voice:", voice)
+        print("ref_audio ", voices[voice]["ref_audio"])
+        voices[voice]["ref_audio"], voices[voice]["ref_text"] = preprocess_ref_audio_text(
+            voices[voice]["ref_audio"], voices[voice]["ref_text"]
+        )
+        print("ref_audio_", voices[voice]["ref_audio"], "\n\n")
+
+    generated_audio_segments = []
+    reg1 = r"(?=\[\w+\])"
+    chunks = re.split(reg1, gen_text)
+    reg2 = r"\[(\w+)\]"
+    for text in chunks:
+        if not text.strip():
+            continue
+        match = re.match(reg2, text)
+        if match:
+            voice = match[1]
+        else:
+            print("No voice tag found, using main.")
+            voice = "main"
+        if voice not in voices:
+            print(f"Voice {voice} not found, using main.")
+            voice = "main"
+        text = re.sub(reg2, "", text)
+        ref_audio_ = voices[voice]["ref_audio"]
+        ref_text_ = voices[voice]["ref_text"]
+        gen_text_ = text.strip()
+        print(f"Voice: {voice}")
+        audio_segment, final_sample_rate, spectrogram = infer_process(
+            ref_audio_,
+            ref_text_,
+            gen_text_,
+            ema_model,
+            vocoder,
+            mel_spec_type=vocoder_name,
+            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=device,
+        )
+        generated_audio_segments.append(audio_segment)
+
+        if save_chunk:
+            if len(gen_text_) > 200:
+                gen_text_ = gen_text_[:200] + " ... "
+            sf.write(
+                os.path.join(output_chunk_dir, f"{len(generated_audio_segments) - 1}_{gen_text_}.wav"),
+                audio_segment,
+                final_sample_rate,
+            )
+
+    if generated_audio_segments:
+        final_wave = np.concatenate(generated_audio_segments)
+
+        if not os.path.exists(output_dir):
+            os.makedirs(output_dir)
+
+        with open(wave_path, "wb") as f:
+            sf.write(f.name, final_wave, final_sample_rate)
+            # Remove silence
+            if remove_silence:
+                remove_silence_for_generated_wav(f.name)
+            print(f.name)
+
+
+if __name__ == "__main__":
+    main()