import gc import json import os import platform import queue import random import re import shutil import signal import subprocess import sys import tempfile import threading import time from glob import glob from importlib.resources import files import click import gradio as gr import librosa import numpy as np import psutil import torch import torchaudio from cached_path import cached_path from datasets import Dataset as Dataset_ from datasets.arrow_writer import ArrowWriter from safetensors.torch import load_file, save_file from scipy.io import wavfile from f5_tts.api import F5TTS from f5_tts.infer.utils_infer import transcribe from f5_tts.model.utils import convert_char_to_pinyin training_process = None system = platform.system() python_executable = sys.executable or "python" tts_api = None last_checkpoint = "" last_device = "" last_ema = None path_data = str(files("f5_tts").joinpath("../../data")) path_project_ckpts = str(files("f5_tts").joinpath("../../ckpts")) file_train = str(files("f5_tts").joinpath("train/finetune_cli.py")) device = ( "cuda" if torch.cuda.is_available() else "xpu" if torch.xpu.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" ) # Save settings from a JSON file def save_settings( project_name, exp_name, learning_rate, batch_size_per_gpu, batch_size_type, max_samples, grad_accumulation_steps, max_grad_norm, epochs, num_warmup_updates, save_per_updates, keep_last_n_checkpoints, last_per_updates, finetune, file_checkpoint_train, tokenizer_type, tokenizer_file, mixed_precision, logger, ch_8bit_adam, ): path_project = os.path.join(path_project_ckpts, project_name) os.makedirs(path_project, exist_ok=True) file_setting = os.path.join(path_project, "setting.json") settings = { "exp_name": exp_name, "learning_rate": learning_rate, "batch_size_per_gpu": batch_size_per_gpu, "batch_size_type": batch_size_type, "max_samples": max_samples, "grad_accumulation_steps": grad_accumulation_steps, "max_grad_norm": max_grad_norm, "epochs": epochs, "num_warmup_updates": num_warmup_updates, "save_per_updates": save_per_updates, "keep_last_n_checkpoints": keep_last_n_checkpoints, "last_per_updates": last_per_updates, "finetune": finetune, "file_checkpoint_train": file_checkpoint_train, "tokenizer_type": tokenizer_type, "tokenizer_file": tokenizer_file, "mixed_precision": mixed_precision, "logger": logger, "bnb_optimizer": ch_8bit_adam, } with open(file_setting, "w") as f: json.dump(settings, f, indent=4) return "Settings saved!" # Load settings from a JSON file def load_settings(project_name): project_name = project_name.replace("_pinyin", "").replace("_char", "") path_project = os.path.join(path_project_ckpts, project_name) file_setting = os.path.join(path_project, "setting.json") # Default settings default_settings = { "exp_name": "F5TTS_v1_Base", "learning_rate": 1e-5, "batch_size_per_gpu": 3200, "batch_size_type": "frame", "max_samples": 64, "grad_accumulation_steps": 1, "max_grad_norm": 1.0, "epochs": 100, "num_warmup_updates": 100, "save_per_updates": 500, "keep_last_n_checkpoints": -1, "last_per_updates": 100, "finetune": True, "file_checkpoint_train": "", "tokenizer_type": "pinyin", "tokenizer_file": "", "mixed_precision": "fp16", "logger": "none", "bnb_optimizer": False, } if device == "mps": default_settings["mixed_precision"] = "none" # Load settings from file if it exists if os.path.isfile(file_setting): with open(file_setting, "r") as f: file_settings = json.load(f) default_settings.update(file_settings) # Return as a tuple in the correct order return ( default_settings["exp_name"], default_settings["learning_rate"], default_settings["batch_size_per_gpu"], default_settings["batch_size_type"], default_settings["max_samples"], default_settings["grad_accumulation_steps"], default_settings["max_grad_norm"], default_settings["epochs"], default_settings["num_warmup_updates"], default_settings["save_per_updates"], default_settings["keep_last_n_checkpoints"], default_settings["last_per_updates"], default_settings["finetune"], default_settings["file_checkpoint_train"], default_settings["tokenizer_type"], default_settings["tokenizer_file"], default_settings["mixed_precision"], default_settings["logger"], default_settings["bnb_optimizer"], ) # Load metadata def get_audio_duration(audio_path): """Calculate the duration mono of an audio file.""" audio, sample_rate = torchaudio.load(audio_path) return audio.shape[1] / sample_rate class Slicer: # https://github.com/RVC-Boss/GPT-SoVITS/blob/main/tools/slicer2.py def __init__( self, sr: int, threshold: float = -40.0, min_length: int = 20000, # 20 seconds min_interval: int = 300, hop_size: int = 20, max_sil_kept: int = 2000, ): if not min_length >= min_interval >= hop_size: raise ValueError("The following condition must be satisfied: min_length >= min_interval >= hop_size") if not max_sil_kept >= hop_size: raise ValueError("The following condition must be satisfied: max_sil_kept >= hop_size") min_interval = sr * min_interval / 1000 self.threshold = 10 ** (threshold / 20.0) self.hop_size = round(sr * hop_size / 1000) self.win_size = min(round(min_interval), 4 * self.hop_size) self.min_length = round(sr * min_length / 1000 / self.hop_size) self.min_interval = round(min_interval / self.hop_size) self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size) def _apply_slice(self, waveform, begin, end): if len(waveform.shape) > 1: return waveform[:, begin * self.hop_size : min(waveform.shape[1], end * self.hop_size)] else: return waveform[begin * self.hop_size : min(waveform.shape[0], end * self.hop_size)] # @timeit def slice(self, waveform): if len(waveform.shape) > 1: samples = waveform.mean(axis=0) else: samples = waveform if samples.shape[0] <= self.min_length: return [waveform] rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0) sil_tags = [] silence_start = None clip_start = 0 for i, rms in enumerate(rms_list): # Keep looping while frame is silent. if rms < self.threshold: # Record start of silent frames. if silence_start is None: silence_start = i continue # Keep looping while frame is not silent and silence start has not been recorded. if silence_start is None: continue # Clear recorded silence start if interval is not enough or clip is too short is_leading_silence = silence_start == 0 and i > self.max_sil_kept need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length if not is_leading_silence and not need_slice_middle: silence_start = None continue # Need slicing. Record the range of silent frames to be removed. if i - silence_start <= self.max_sil_kept: pos = rms_list[silence_start : i + 1].argmin() + silence_start if silence_start == 0: sil_tags.append((0, pos)) else: sil_tags.append((pos, pos)) clip_start = pos elif i - silence_start <= self.max_sil_kept * 2: pos = rms_list[i - self.max_sil_kept : silence_start + self.max_sil_kept + 1].argmin() pos += i - self.max_sil_kept pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept if silence_start == 0: sil_tags.append((0, pos_r)) clip_start = pos_r else: sil_tags.append((min(pos_l, pos), max(pos_r, pos))) clip_start = max(pos_r, pos) else: pos_l = rms_list[silence_start : silence_start + self.max_sil_kept + 1].argmin() + silence_start pos_r = rms_list[i - self.max_sil_kept : i + 1].argmin() + i - self.max_sil_kept if silence_start == 0: sil_tags.append((0, pos_r)) else: sil_tags.append((pos_l, pos_r)) clip_start = pos_r silence_start = None # Deal with trailing silence. total_frames = rms_list.shape[0] if silence_start is not None and total_frames - silence_start >= self.min_interval: silence_end = min(total_frames, silence_start + self.max_sil_kept) pos = rms_list[silence_start : silence_end + 1].argmin() + silence_start sil_tags.append((pos, total_frames + 1)) # Apply and return slices: [chunk, start, end] if len(sil_tags) == 0: return [[waveform, 0, int(total_frames * self.hop_size)]] else: chunks = [] if sil_tags[0][0] > 0: chunks.append([self._apply_slice(waveform, 0, sil_tags[0][0]), 0, int(sil_tags[0][0] * self.hop_size)]) for i in range(len(sil_tags) - 1): chunks.append( [ self._apply_slice(waveform, sil_tags[i][1], sil_tags[i + 1][0]), int(sil_tags[i][1] * self.hop_size), int(sil_tags[i + 1][0] * self.hop_size), ] ) if sil_tags[-1][1] < total_frames: chunks.append( [ self._apply_slice(waveform, sil_tags[-1][1], total_frames), int(sil_tags[-1][1] * self.hop_size), int(total_frames * self.hop_size), ] ) return chunks # terminal def terminate_process_tree(pid, including_parent=True): try: parent = psutil.Process(pid) except psutil.NoSuchProcess: # Process already terminated return children = parent.children(recursive=True) for child in children: try: os.kill(child.pid, signal.SIGTERM) # or signal.SIGKILL except OSError: pass if including_parent: try: os.kill(parent.pid, signal.SIGTERM) # or signal.SIGKILL except OSError: pass def terminate_process(pid): if system == "Windows": cmd = f"taskkill /t /f /pid {pid}" os.system(cmd) else: terminate_process_tree(pid) def start_training( dataset_name, exp_name, learning_rate, batch_size_per_gpu, batch_size_type, max_samples, grad_accumulation_steps, max_grad_norm, epochs, num_warmup_updates, save_per_updates, keep_last_n_checkpoints, last_per_updates, finetune, file_checkpoint_train, tokenizer_type, tokenizer_file, mixed_precision, stream, logger, ch_8bit_adam, ): global training_process, tts_api, stop_signal if tts_api is not None: if tts_api is not None: del tts_api gc.collect() torch.cuda.empty_cache() tts_api = None path_project = os.path.join(path_data, dataset_name) if not os.path.isdir(path_project): yield ( f"There is not project with name {dataset_name}", gr.update(interactive=True), gr.update(interactive=False), ) return file_raw = os.path.join(path_project, "raw.arrow") if not os.path.isfile(file_raw): yield f"There is no file {file_raw}", gr.update(interactive=True), gr.update(interactive=False) return # Check if a training process is already running if training_process is not None: return "Train run already!", gr.update(interactive=False), gr.update(interactive=True) yield "start train", gr.update(interactive=False), gr.update(interactive=False) # Command to run the training script with the specified arguments if tokenizer_file == "": if dataset_name.endswith("_pinyin"): tokenizer_type = "pinyin" elif dataset_name.endswith("_char"): tokenizer_type = "char" else: tokenizer_type = "custom" dataset_name = dataset_name.replace("_pinyin", "").replace("_char", "") if mixed_precision != "none": fp16 = f"--mixed_precision={mixed_precision}" else: fp16 = "" cmd = ( f'accelerate launch {fp16} "{file_train}" --exp_name {exp_name}' f" --learning_rate {learning_rate}" f" --batch_size_per_gpu {batch_size_per_gpu}" f" --batch_size_type {batch_size_type}" f" --max_samples {max_samples}" f" --grad_accumulation_steps {grad_accumulation_steps}" f" --max_grad_norm {max_grad_norm}" f" --epochs {epochs}" f" --num_warmup_updates {num_warmup_updates}" f" --save_per_updates {save_per_updates}" f" --keep_last_n_checkpoints {keep_last_n_checkpoints}" f" --last_per_updates {last_per_updates}" f" --dataset_name {dataset_name}" ) if finetune: cmd += " --finetune" if file_checkpoint_train != "": cmd += f' --pretrain "{file_checkpoint_train}"' if tokenizer_file != "": cmd += f" --tokenizer_path {tokenizer_file}" cmd += f" --tokenizer {tokenizer_type}" if logger != "none": cmd += f" --logger {logger}" cmd += " --log_samples" if ch_8bit_adam: cmd += " --bnb_optimizer" print("run command : \n" + cmd + "\n") save_settings( dataset_name, exp_name, learning_rate, batch_size_per_gpu, batch_size_type, max_samples, grad_accumulation_steps, max_grad_norm, epochs, num_warmup_updates, save_per_updates, keep_last_n_checkpoints, last_per_updates, finetune, file_checkpoint_train, tokenizer_type, tokenizer_file, mixed_precision, logger, ch_8bit_adam, ) try: if not stream: # Start the training process training_process = subprocess.Popen(cmd, shell=True) time.sleep(5) yield "train start", gr.update(interactive=False), gr.update(interactive=True) # Wait for the training process to finish training_process.wait() else: def stream_output(pipe, output_queue): try: for line in iter(pipe.readline, ""): output_queue.put(line) except Exception as e: output_queue.put(f"Error reading pipe: {str(e)}") finally: pipe.close() env = os.environ.copy() env["PYTHONUNBUFFERED"] = "1" training_process = subprocess.Popen( cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, bufsize=1, env=env ) yield "Training started ...", gr.update(interactive=False), gr.update(interactive=True) stdout_queue = queue.Queue() stderr_queue = queue.Queue() stdout_thread = threading.Thread(target=stream_output, args=(training_process.stdout, stdout_queue)) stderr_thread = threading.Thread(target=stream_output, args=(training_process.stderr, stderr_queue)) stdout_thread.daemon = True stderr_thread.daemon = True stdout_thread.start() stderr_thread.start() stop_signal = False while True: if stop_signal: training_process.terminate() time.sleep(0.5) if training_process.poll() is None: training_process.kill() yield "Training stopped by user.", gr.update(interactive=True), gr.update(interactive=False) break process_status = training_process.poll() # Handle stdout try: while True: output = stdout_queue.get_nowait() print(output, end="") match = re.search( r"Epoch (\d+)/(\d+):\s+(\d+)%\|.*\[(\d+:\d+)<.*?loss=(\d+\.\d+), update=(\d+)", output ) if match: current_epoch = match.group(1) total_epochs = match.group(2) percent_complete = match.group(3) elapsed_time = match.group(4) loss = match.group(5) current_update = match.group(6) message = ( f"Epoch: {current_epoch}/{total_epochs}, " f"Progress: {percent_complete}%, " f"Elapsed Time: {elapsed_time}, " f"Loss: {loss}, " f"Update: {current_update}" ) yield message, gr.update(interactive=False), gr.update(interactive=True) elif output.strip(): yield output, gr.update(interactive=False), gr.update(interactive=True) except queue.Empty: pass # Handle stderr try: while True: error_output = stderr_queue.get_nowait() print(error_output, end="") if error_output.strip(): yield f"{error_output.strip()}", gr.update(interactive=False), gr.update(interactive=True) except queue.Empty: pass if process_status is not None and stdout_queue.empty() and stderr_queue.empty(): if process_status != 0: yield ( f"Process crashed with exit code {process_status}!", gr.update(interactive=False), gr.update(interactive=True), ) else: yield ( "Training complete or paused ...", gr.update(interactive=False), gr.update(interactive=True), ) break # Small sleep to prevent CPU thrashing time.sleep(0.1) # Clean up training_process.stdout.close() training_process.stderr.close() training_process.wait() time.sleep(1) if training_process is None: text_info = "Train stopped !" else: text_info = "Train complete at end !" except Exception as e: # Catch all exceptions # Ensure that we reset the training process variable in case of an error text_info = f"An error occurred: {str(e)}" training_process = None yield text_info, gr.update(interactive=True), gr.update(interactive=False) def stop_training(): global training_process, stop_signal if training_process is None: return "Train not running !", gr.update(interactive=True), gr.update(interactive=False) terminate_process_tree(training_process.pid) # training_process = None stop_signal = True return "Train stopped !", gr.update(interactive=True), gr.update(interactive=False) def get_list_projects(): project_list = [] for folder in os.listdir(path_data): path_folder = os.path.join(path_data, folder) if not os.path.isdir(path_folder): continue folder = folder.lower() if folder == "emilia_zh_en_pinyin": continue project_list.append(folder) projects_selelect = None if not project_list else project_list[-1] return project_list, projects_selelect def create_data_project(name, tokenizer_type): name += "_" + tokenizer_type os.makedirs(os.path.join(path_data, name), exist_ok=True) os.makedirs(os.path.join(path_data, name, "dataset"), exist_ok=True) project_list, projects_selelect = get_list_projects() return gr.update(choices=project_list, value=name) def transcribe_all(name_project, audio_files, language, user=False, progress=gr.Progress()): path_project = os.path.join(path_data, name_project) path_dataset = os.path.join(path_project, "dataset") path_project_wavs = os.path.join(path_project, "wavs") file_metadata = os.path.join(path_project, "metadata.csv") if not user: if audio_files is None: return "You need to load an audio file." if os.path.isdir(path_project_wavs): shutil.rmtree(path_project_wavs) if os.path.isfile(file_metadata): os.remove(file_metadata) os.makedirs(path_project_wavs, exist_ok=True) if user: file_audios = [ file for format in ("*.wav", "*.ogg", "*.opus", "*.mp3", "*.flac") for file in glob(os.path.join(path_dataset, format)) ] if file_audios == []: return "No audio file was found in the dataset." else: file_audios = audio_files alpha = 0.5 _max = 1.0 slicer = Slicer(24000) num = 0 error_num = 0 data = "" for file_audio in progress.tqdm(file_audios, desc="transcribe files", total=len((file_audios))): audio, _ = librosa.load(file_audio, sr=24000, mono=True) list_slicer = slicer.slice(audio) for chunk, start, end in progress.tqdm(list_slicer, total=len(list_slicer), desc="slicer files"): name_segment = os.path.join(f"segment_{num}") file_segment = os.path.join(path_project_wavs, f"{name_segment}.wav") tmp_max = np.abs(chunk).max() if tmp_max > 1: chunk /= tmp_max chunk = (chunk / tmp_max * (_max * alpha)) + (1 - alpha) * chunk wavfile.write(file_segment, 24000, (chunk * 32767).astype(np.int16)) try: text = transcribe(file_segment, language) text = text.strip() data += f"{name_segment}|{text}\n" num += 1 except: # noqa: E722 error_num += 1 with open(file_metadata, "w", encoding="utf-8-sig") as f: f.write(data) if error_num != []: error_text = f"\nerror files : {error_num}" else: error_text = "" return f"transcribe complete samples : {num}\npath : {path_project_wavs}{error_text}" def format_seconds_to_hms(seconds): hours = int(seconds / 3600) minutes = int((seconds % 3600) / 60) seconds = seconds % 60 return "{:02d}:{:02d}:{:02d}".format(hours, minutes, int(seconds)) def get_correct_audio_path( audio_input, base_path="wavs", supported_formats=("wav", "mp3", "aac", "flac", "m4a", "alac", "ogg", "aiff", "wma", "amr"), ): file_audio = None # Helper function to check if file has a supported extension def has_supported_extension(file_name): return any(file_name.endswith(f".{ext}") for ext in supported_formats) # Case 1: If it's a full path with a valid extension, use it directly if os.path.isabs(audio_input) and has_supported_extension(audio_input): file_audio = audio_input # Case 2: If it has a supported extension but is not a full path elif has_supported_extension(audio_input) and not os.path.isabs(audio_input): file_audio = os.path.join(base_path, audio_input) # Case 3: If only the name is given (no extension and not a full path) elif not has_supported_extension(audio_input) and not os.path.isabs(audio_input): for ext in supported_formats: potential_file = os.path.join(base_path, f"{audio_input}.{ext}") if os.path.exists(potential_file): file_audio = potential_file break else: file_audio = os.path.join(base_path, f"{audio_input}.{supported_formats[0]}") return file_audio def create_metadata(name_project, ch_tokenizer, progress=gr.Progress()): path_project = os.path.join(path_data, name_project) path_project_wavs = os.path.join(path_project, "wavs") file_metadata = os.path.join(path_project, "metadata.csv") file_raw = os.path.join(path_project, "raw.arrow") file_duration = os.path.join(path_project, "duration.json") file_vocab = os.path.join(path_project, "vocab.txt") if not os.path.isfile(file_metadata): return "The file was not found in " + file_metadata, "" with open(file_metadata, "r", encoding="utf-8-sig") as f: data = f.read() audio_path_list = [] text_list = [] duration_list = [] count = data.split("\n") lenght = 0 result = [] error_files = [] text_vocab_set = set() for line in progress.tqdm(data.split("\n"), total=count): sp_line = line.split("|") if len(sp_line) != 2: continue name_audio, text = sp_line[:2] file_audio = get_correct_audio_path(name_audio, path_project_wavs) if not os.path.isfile(file_audio): error_files.append([file_audio, "error path"]) continue try: duration = get_audio_duration(file_audio) except Exception as e: error_files.append([file_audio, "duration"]) print(f"Error processing {file_audio}: {e}") continue if duration < 1 or duration > 30: if duration > 30: error_files.append([file_audio, "duration > 30 sec"]) if duration < 1: error_files.append([file_audio, "duration < 1 sec "]) continue if len(text) < 3: error_files.append([file_audio, "very short text length 3"]) continue text = text.strip() text = convert_char_to_pinyin([text], polyphone=True)[0] audio_path_list.append(file_audio) duration_list.append(duration) text_list.append(text) result.append({"audio_path": file_audio, "text": text, "duration": duration}) if ch_tokenizer: text_vocab_set.update(list(text)) lenght += duration if duration_list == []: return f"Error: No audio files found in the specified path : {path_project_wavs}", "" min_second = round(min(duration_list), 2) max_second = round(max(duration_list), 2) with ArrowWriter(path=file_raw) as writer: for line in progress.tqdm(result, total=len(result), desc="prepare data"): writer.write(line) writer.finalize() with open(file_duration, "w") as f: json.dump({"duration": duration_list}, f, ensure_ascii=False) new_vocal = "" if not ch_tokenizer: if not os.path.isfile(file_vocab): file_vocab_finetune = os.path.join(path_data, "Emilia_ZH_EN_pinyin/vocab.txt") if not os.path.isfile(file_vocab_finetune): return "Error: Vocabulary file 'Emilia_ZH_EN_pinyin' not found!", "" shutil.copy2(file_vocab_finetune, file_vocab) with open(file_vocab, "r", encoding="utf-8-sig") as f: vocab_char_map = {} for i, char in enumerate(f): vocab_char_map[char[:-1]] = i vocab_size = len(vocab_char_map) else: with open(file_vocab, "w", encoding="utf-8-sig") as f: for vocab in sorted(text_vocab_set): f.write(vocab + "\n") new_vocal += vocab + "\n" vocab_size = len(text_vocab_set) if error_files != []: error_text = "\n".join([" = ".join(item) for item in error_files]) else: error_text = "" return ( f"prepare complete \nsamples : {len(text_list)}\ntime data : {format_seconds_to_hms(lenght)}\nmin sec : {min_second}\nmax sec : {max_second}\nfile_arrow : {file_raw}\nvocab : {vocab_size}\n{error_text}", new_vocal, ) def check_user(value): return gr.update(visible=not value), gr.update(visible=value) def calculate_train( name_project, epochs, learning_rate, batch_size_per_gpu, batch_size_type, max_samples, num_warmup_updates, finetune, ): path_project = os.path.join(path_data, name_project) file_duration = os.path.join(path_project, "duration.json") hop_length = 256 sampling_rate = 24000 if not os.path.isfile(file_duration): return ( epochs, learning_rate, batch_size_per_gpu, max_samples, num_warmup_updates, "project not found !", ) with open(file_duration, "r") as file: data = json.load(file) duration_list = data["duration"] max_sample_length = max(duration_list) * sampling_rate / hop_length total_samples = len(duration_list) total_duration = sum(duration_list) if torch.cuda.is_available(): gpu_count = torch.cuda.device_count() total_memory = 0 for i in range(gpu_count): gpu_properties = torch.cuda.get_device_properties(i) total_memory += gpu_properties.total_memory / (1024**3) # in GB elif torch.xpu.is_available(): gpu_count = torch.xpu.device_count() total_memory = 0 for i in range(gpu_count): gpu_properties = torch.xpu.get_device_properties(i) total_memory += gpu_properties.total_memory / (1024**3) elif torch.backends.mps.is_available(): gpu_count = 1 total_memory = psutil.virtual_memory().available / (1024**3) avg_gpu_memory = total_memory / gpu_count # rough estimate of batch size if batch_size_type == "frame": batch_size_per_gpu = max(int(38400 * (avg_gpu_memory - 5) / 75), int(max_sample_length)) elif batch_size_type == "sample": batch_size_per_gpu = int(200 / (total_duration / total_samples)) if total_samples < 64: max_samples = int(total_samples * 0.25) num_warmup_updates = max(num_warmup_updates, int(total_samples * 0.05)) # take 1.2M updates as the maximum max_updates = 1200000 if batch_size_type == "frame": mini_batch_duration = batch_size_per_gpu * gpu_count * hop_length / sampling_rate updates_per_epoch = total_duration / mini_batch_duration elif batch_size_type == "sample": updates_per_epoch = total_samples / batch_size_per_gpu / gpu_count epochs = int(max_updates / updates_per_epoch) if finetune: learning_rate = 1e-5 else: learning_rate = 7.5e-5 return ( epochs, learning_rate, batch_size_per_gpu, max_samples, num_warmup_updates, total_samples, ) def prune_checkpoint(checkpoint_path: str, new_checkpoint_path: str, save_ema: bool, safetensors: bool) -> str: try: checkpoint = torch.load(checkpoint_path, weights_only=True) print("Original Checkpoint Keys:", checkpoint.keys()) to_retain = "ema_model_state_dict" if save_ema else "model_state_dict" try: model_state_dict_to_retain = checkpoint[to_retain] except KeyError: return f"{to_retain} not found in the checkpoint." if safetensors: new_checkpoint_path = new_checkpoint_path.replace(".pt", ".safetensors") save_file(model_state_dict_to_retain, new_checkpoint_path) else: new_checkpoint_path = new_checkpoint_path.replace(".safetensors", ".pt") new_checkpoint = {"ema_model_state_dict": model_state_dict_to_retain} torch.save(new_checkpoint, new_checkpoint_path) return f"New checkpoint saved at: {new_checkpoint_path}" except Exception as e: return f"An error occurred: {e}" def expand_model_embeddings(ckpt_path, new_ckpt_path, num_new_tokens=42): seed = 666 random.seed(seed) os.environ["PYTHONHASHSEED"] = str(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False if ckpt_path.endswith(".safetensors"): ckpt = load_file(ckpt_path, device="cpu") ckpt = {"ema_model_state_dict": ckpt} elif ckpt_path.endswith(".pt"): ckpt = torch.load(ckpt_path, map_location="cpu") ema_sd = ckpt.get("ema_model_state_dict", {}) embed_key_ema = "ema_model.transformer.text_embed.text_embed.weight" old_embed_ema = ema_sd[embed_key_ema] vocab_old = old_embed_ema.size(0) embed_dim = old_embed_ema.size(1) vocab_new = vocab_old + num_new_tokens def expand_embeddings(old_embeddings): new_embeddings = torch.zeros((vocab_new, embed_dim)) new_embeddings[:vocab_old] = old_embeddings new_embeddings[vocab_old:] = torch.randn((num_new_tokens, embed_dim)) return new_embeddings ema_sd[embed_key_ema] = expand_embeddings(ema_sd[embed_key_ema]) if new_ckpt_path.endswith(".safetensors"): save_file(ema_sd, new_ckpt_path) elif new_ckpt_path.endswith(".pt"): torch.save(ckpt, new_ckpt_path) return vocab_new def vocab_count(text): return str(len(text.split(","))) def vocab_extend(project_name, symbols, model_type): if symbols == "": return "Symbols empty!" name_project = project_name path_project = os.path.join(path_data, name_project) file_vocab_project = os.path.join(path_project, "vocab.txt") file_vocab = os.path.join(path_data, "Emilia_ZH_EN_pinyin/vocab.txt") if not os.path.isfile(file_vocab): return f"the file {file_vocab} not found !" symbols = symbols.split(",") if symbols == []: return "Symbols to extend not found." with open(file_vocab, "r", encoding="utf-8-sig") as f: data = f.read() vocab = data.split("\n") vocab_check = set(vocab) miss_symbols = [] for item in symbols: item = item.replace(" ", "") if item in vocab_check: continue miss_symbols.append(item) if miss_symbols == []: return "Symbols are okay no need to extend." size_vocab = len(vocab) vocab.pop() for item in miss_symbols: vocab.append(item) vocab.append("") with open(file_vocab_project, "w", encoding="utf-8") as f: f.write("\n".join(vocab)) if model_type == "F5TTS_v1_Base": ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_v1_Base/model_1250000.safetensors")) elif model_type == "F5TTS_Base": ckpt_path = str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.pt")) elif model_type == "E2TTS_Base": ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.pt")) vocab_size_new = len(miss_symbols) dataset_name = name_project.replace("_pinyin", "").replace("_char", "") new_ckpt_path = os.path.join(path_project_ckpts, dataset_name) os.makedirs(new_ckpt_path, exist_ok=True) # Add pretrained_ prefix to model when copying for consistency with finetune_cli.py new_ckpt_file = os.path.join(new_ckpt_path, "pretrained_" + os.path.basename(ckpt_path)) size = expand_model_embeddings(ckpt_path, new_ckpt_file, num_new_tokens=vocab_size_new) vocab_new = "\n".join(miss_symbols) return f"vocab old size : {size_vocab}\nvocab new size : {size}\nvocab add : {vocab_size_new}\nnew symbols :\n{vocab_new}" def vocab_check(project_name, tokenizer_type): name_project = project_name path_project = os.path.join(path_data, name_project) file_metadata = os.path.join(path_project, "metadata.csv") file_vocab = os.path.join(path_data, "Emilia_ZH_EN_pinyin/vocab.txt") if not os.path.isfile(file_vocab): return f"the file {file_vocab} not found !", "" with open(file_vocab, "r", encoding="utf-8-sig") as f: data = f.read() vocab = data.split("\n") vocab = set(vocab) if not os.path.isfile(file_metadata): return f"the file {file_metadata} not found !", "" with open(file_metadata, "r", encoding="utf-8-sig") as f: data = f.read() miss_symbols = [] miss_symbols_keep = {} for item in data.split("\n"): sp = item.split("|") if len(sp) != 2: continue text = sp[1].strip() if tokenizer_type == "pinyin": text = convert_char_to_pinyin([text], polyphone=True)[0] for t in text: if t not in vocab and t not in miss_symbols_keep: miss_symbols.append(t) miss_symbols_keep[t] = t if miss_symbols == []: vocab_miss = "" info = "You can train using your language !" else: vocab_miss = ",".join(miss_symbols) info = f"The following {len(miss_symbols)} symbols are missing in your language\n\n" return info, vocab_miss def get_random_sample_prepare(project_name): name_project = project_name path_project = os.path.join(path_data, name_project) file_arrow = os.path.join(path_project, "raw.arrow") if not os.path.isfile(file_arrow): return "", None dataset = Dataset_.from_file(file_arrow) random_sample = dataset.shuffle(seed=random.randint(0, 1000)).select([0]) text = "[" + " , ".join(["' " + t + " '" for t in random_sample["text"][0]]) + "]" audio_path = random_sample["audio_path"][0] return text, audio_path def get_random_sample_transcribe(project_name): name_project = project_name path_project = os.path.join(path_data, name_project) file_metadata = os.path.join(path_project, "metadata.csv") if not os.path.isfile(file_metadata): return "", None data = "" with open(file_metadata, "r", encoding="utf-8-sig") as f: data = f.read() list_data = [] for item in data.split("\n"): sp = item.split("|") if len(sp) != 2: continue # fixed audio when it is absolute file_audio = get_correct_audio_path(sp[0], os.path.join(path_project, "wavs")) list_data.append([file_audio, sp[1]]) if list_data == []: return "", None random_item = random.choice(list_data) return random_item[1], random_item[0] def get_random_sample_infer(project_name): text, audio = get_random_sample_transcribe(project_name) return ( text, text, audio, ) def infer( project, file_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step, use_ema, speed, seed, remove_silence ): global last_checkpoint, last_device, tts_api, last_ema if not os.path.isfile(file_checkpoint): return None, "checkpoint not found!" if training_process is not None: device_test = "cpu" else: device_test = None if last_checkpoint != file_checkpoint or last_device != device_test or last_ema != use_ema or tts_api is None: if last_checkpoint != file_checkpoint: last_checkpoint = file_checkpoint if last_device != device_test: last_device = device_test if last_ema != use_ema: last_ema = use_ema vocab_file = os.path.join(path_data, project, "vocab.txt") tts_api = F5TTS( model=exp_name, ckpt_file=file_checkpoint, vocab_file=vocab_file, device=device_test, use_ema=use_ema ) print("update >> ", device_test, file_checkpoint, use_ema) if seed == -1: # -1 used for random seed = None with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: tts_api.infer( ref_file=ref_audio, ref_text=ref_text.strip(), gen_text=gen_text.strip(), nfe_step=nfe_step, speed=speed, remove_silence=remove_silence, file_wave=f.name, seed=seed, ) return f.name, tts_api.device, str(tts_api.seed) def check_finetune(finetune): return gr.update(interactive=finetune), gr.update(interactive=finetune), gr.update(interactive=finetune) def get_checkpoints_project(project_name, is_gradio=True): if project_name is None: return [], "" project_name = project_name.replace("_pinyin", "").replace("_char", "") if os.path.isdir(path_project_ckpts): files_checkpoints = glob(os.path.join(path_project_ckpts, project_name, "*.pt")) # Separate pretrained and regular checkpoints pretrained_checkpoints = [f for f in files_checkpoints if "pretrained_" in os.path.basename(f)] regular_checkpoints = [ f for f in files_checkpoints if "pretrained_" not in os.path.basename(f) and "model_last.pt" not in os.path.basename(f) ] last_checkpoint = [f for f in files_checkpoints if "model_last.pt" in os.path.basename(f)] # Sort regular checkpoints by number regular_checkpoints = sorted( regular_checkpoints, key=lambda x: int(os.path.basename(x).split("_")[1].split(".")[0]) ) # Combine in order: pretrained, regular, last files_checkpoints = pretrained_checkpoints + regular_checkpoints + last_checkpoint else: files_checkpoints = [] selelect_checkpoint = None if not files_checkpoints else files_checkpoints[0] if is_gradio: return gr.update(choices=files_checkpoints, value=selelect_checkpoint) return files_checkpoints, selelect_checkpoint def get_audio_project(project_name, is_gradio=True): if project_name is None: return [], "" project_name = project_name.replace("_pinyin", "").replace("_char", "") if os.path.isdir(path_project_ckpts): files_audios = glob(os.path.join(path_project_ckpts, project_name, "samples", "*.wav")) files_audios = sorted(files_audios, key=lambda x: int(os.path.basename(x).split("_")[1].split(".")[0])) files_audios = [item.replace("_gen.wav", "") for item in files_audios if item.endswith("_gen.wav")] else: files_audios = [] selelect_checkpoint = None if not files_audios else files_audios[0] if is_gradio: return gr.update(choices=files_audios, value=selelect_checkpoint) return files_audios, selelect_checkpoint def get_gpu_stats(): gpu_stats = "" if torch.cuda.is_available(): gpu_count = torch.cuda.device_count() for i in range(gpu_count): gpu_name = torch.cuda.get_device_name(i) gpu_properties = torch.cuda.get_device_properties(i) total_memory = gpu_properties.total_memory / (1024**3) # in GB allocated_memory = torch.cuda.memory_allocated(i) / (1024**2) # in MB reserved_memory = torch.cuda.memory_reserved(i) / (1024**2) # in MB gpu_stats += ( f"GPU {i} Name: {gpu_name}\n" f"Total GPU memory (GPU {i}): {total_memory:.2f} GB\n" f"Allocated GPU memory (GPU {i}): {allocated_memory:.2f} MB\n" f"Reserved GPU memory (GPU {i}): {reserved_memory:.2f} MB\n\n" ) elif torch.xpu.is_available(): gpu_count = torch.xpu.device_count() for i in range(gpu_count): gpu_name = torch.xpu.get_device_name(i) gpu_properties = torch.xpu.get_device_properties(i) total_memory = gpu_properties.total_memory / (1024**3) # in GB allocated_memory = torch.xpu.memory_allocated(i) / (1024**2) # in MB reserved_memory = torch.xpu.memory_reserved(i) / (1024**2) # in MB gpu_stats += ( f"GPU {i} Name: {gpu_name}\n" f"Total GPU memory (GPU {i}): {total_memory:.2f} GB\n" f"Allocated GPU memory (GPU {i}): {allocated_memory:.2f} MB\n" f"Reserved GPU memory (GPU {i}): {reserved_memory:.2f} MB\n\n" ) elif torch.backends.mps.is_available(): gpu_count = 1 gpu_stats += "MPS GPU\n" total_memory = psutil.virtual_memory().total / ( 1024**3 ) # Total system memory (MPS doesn't have its own memory) allocated_memory = 0 reserved_memory = 0 gpu_stats += ( f"Total system memory: {total_memory:.2f} GB\n" f"Allocated GPU memory (MPS): {allocated_memory:.2f} MB\n" f"Reserved GPU memory (MPS): {reserved_memory:.2f} MB\n" ) else: gpu_stats = "No GPU available" return gpu_stats def get_cpu_stats(): cpu_usage = psutil.cpu_percent(interval=1) memory_info = psutil.virtual_memory() memory_used = memory_info.used / (1024**2) memory_total = memory_info.total / (1024**2) memory_percent = memory_info.percent pid = os.getpid() process = psutil.Process(pid) nice_value = process.nice() cpu_stats = ( f"CPU Usage: {cpu_usage:.2f}%\n" f"System Memory: {memory_used:.2f} MB used / {memory_total:.2f} MB total ({memory_percent}% used)\n" f"Process Priority (Nice value): {nice_value}" ) return cpu_stats def get_combined_stats(): gpu_stats = get_gpu_stats() cpu_stats = get_cpu_stats() combined_stats = f"### GPU Stats\n{gpu_stats}\n\n### CPU Stats\n{cpu_stats}" return combined_stats def get_audio_select(file_sample): select_audio_ref = file_sample select_audio_gen = file_sample if file_sample is not None: select_audio_ref += "_ref.wav" select_audio_gen += "_gen.wav" return select_audio_ref, select_audio_gen with gr.Blocks() as app: gr.Markdown( """ # F5 TTS Automatic Finetune This is a local web UI for F5 TTS finetuning support. This app supports the following TTS models: * [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching) * [E2 TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS) The pretrained checkpoints support English and Chinese. For tutorial and updates check here (https://github.com/SWivid/F5-TTS/discussions/143) """ ) with gr.Row(): projects, projects_selelect = get_list_projects() tokenizer_type = gr.Radio(label="Tokenizer Type", choices=["pinyin", "char", "custom"], value="pinyin") project_name = gr.Textbox(label="Project Name", value="my_speak") bt_create = gr.Button("Create a New Project") with gr.Row(): cm_project = gr.Dropdown( choices=projects, value=projects_selelect, label="Project", allow_custom_value=True, scale=6 ) ch_refresh_project = gr.Button("Refresh", scale=1) bt_create.click(fn=create_data_project, inputs=[project_name, tokenizer_type], outputs=[cm_project]) with gr.Tabs(): with gr.TabItem("Transcribe Data"): gr.Markdown("""```plaintext Skip this step if you have your dataset, metadata.csv, and a folder wavs with all the audio files. ```""") ch_manual = gr.Checkbox(label="Audio from Path", value=False) mark_info_transcribe = gr.Markdown( """```plaintext Place your 'wavs' folder and 'metadata.csv' file in the '{your_project_name}' directory. my_speak/ │ └── dataset/ ├── audio1.wav └── audio2.wav ... ```""", visible=False, ) audio_speaker = gr.File(label="Voice", type="filepath", file_count="multiple") txt_lang = gr.Textbox(label="Language", value="English") bt_transcribe = bt_create = gr.Button("Transcribe") txt_info_transcribe = gr.Textbox(label="Info", value="") bt_transcribe.click( fn=transcribe_all, inputs=[cm_project, audio_speaker, txt_lang, ch_manual], outputs=[txt_info_transcribe], ) ch_manual.change(fn=check_user, inputs=[ch_manual], outputs=[audio_speaker, mark_info_transcribe]) random_sample_transcribe = gr.Button("Random Sample") with gr.Row(): random_text_transcribe = gr.Textbox(label="Text") random_audio_transcribe = gr.Audio(label="Audio", type="filepath") random_sample_transcribe.click( fn=get_random_sample_transcribe, inputs=[cm_project], outputs=[random_text_transcribe, random_audio_transcribe], ) with gr.TabItem("Vocab Check"): gr.Markdown("""```plaintext Check the vocabulary for fine-tuning Emilia_ZH_EN to ensure all symbols are included. For fine-tuning a new language. ```""") check_button = gr.Button("Check Vocab") txt_info_check = gr.Textbox(label="Info", value="") gr.Markdown("""```plaintext Using the extended model, you can finetune to a new language that is missing symbols in the vocab. This creates a new model with a new vocabulary size and saves it in your ckpts/project folder. ```""") exp_name_extend = gr.Radio( label="Model", choices=["F5TTS_v1_Base", "F5TTS_Base", "E2TTS_Base"], value="F5TTS_v1_Base" ) with gr.Row(): txt_extend = gr.Textbox( label="Symbols", value="", placeholder="To add new symbols, make sure to use ',' for each symbol", scale=6, ) txt_count_symbol = gr.Textbox(label="New Vocab Size", value="", scale=1) extend_button = gr.Button("Extend") txt_info_extend = gr.Textbox(label="Info", value="") txt_extend.change(vocab_count, inputs=[txt_extend], outputs=[txt_count_symbol]) check_button.click( fn=vocab_check, inputs=[cm_project, tokenizer_type], outputs=[txt_info_check, txt_extend] ) extend_button.click( fn=vocab_extend, inputs=[cm_project, txt_extend, exp_name_extend], outputs=[txt_info_extend] ) with gr.TabItem("Prepare Data"): gr.Markdown("""```plaintext Skip this step if you have your dataset, raw.arrow, duration.json, and vocab.txt ```""") gr.Markdown( """```plaintext Place all your "wavs" folder and your "metadata.csv" file in your project name directory. Supported audio formats: "wav", "mp3", "aac", "flac", "m4a", "alac", "ogg", "aiff", "wma", "amr" Example wav format: my_speak/ │ ├── wavs/ │ ├── audio1.wav │ └── audio2.wav | ... │ └── metadata.csv File format metadata.csv: audio1|text1 or audio1.wav|text1 or your_path/audio1.wav|text1 audio2|text1 or audio2.wav|text1 or your_path/audio2.wav|text1 ... ```""" ) ch_tokenizern = gr.Checkbox(label="Create Vocabulary", value=False, visible=False) bt_prepare = bt_create = gr.Button("Prepare") txt_info_prepare = gr.Textbox(label="Info", value="") txt_vocab_prepare = gr.Textbox(label="Vocab", value="") bt_prepare.click( fn=create_metadata, inputs=[cm_project, ch_tokenizern], outputs=[txt_info_prepare, txt_vocab_prepare] ) random_sample_prepare = gr.Button("Random Sample") with gr.Row(): random_text_prepare = gr.Textbox(label="Tokenizer") random_audio_prepare = gr.Audio(label="Audio", type="filepath") random_sample_prepare.click( fn=get_random_sample_prepare, inputs=[cm_project], outputs=[random_text_prepare, random_audio_prepare] ) with gr.TabItem("Train Model"): gr.Markdown("""```plaintext The auto-setting is still experimental. Set a large value of epoch if not sure; and keep last N checkpoints if limited disk space. If you encounter a memory error, try reducing the batch size per GPU to a smaller number. ```""") with gr.Row(): exp_name = gr.Radio(label="Model", choices=["F5TTS_v1_Base", "F5TTS_Base", "E2TTS_Base"]) tokenizer_file = gr.Textbox(label="Tokenizer File") file_checkpoint_train = gr.Textbox(label="Path to the Pretrained Checkpoint") with gr.Row(): ch_finetune = bt_create = gr.Checkbox(label="Finetune") lb_samples = gr.Label(label="Samples") bt_calculate = bt_create = gr.Button("Auto Settings") with gr.Row(): epochs = gr.Number(label="Epochs") learning_rate = gr.Number(label="Learning Rate", step=0.5e-5) max_grad_norm = gr.Number(label="Max Gradient Norm") num_warmup_updates = gr.Number(label="Warmup Updates") with gr.Row(): batch_size_type = gr.Radio( label="Batch Size Type", choices=["frame", "sample"], info="frame is calculated as seconds * sampling_rate / hop_length", ) batch_size_per_gpu = gr.Number(label="Batch Size per GPU", info="N frames or N samples") grad_accumulation_steps = gr.Number( label="Gradient Accumulation Steps", info="Effective batch size is multiplied by this value" ) max_samples = gr.Number(label="Max Samples", info="Maximum number of samples per single GPU batch") with gr.Row(): save_per_updates = gr.Number( label="Save per Updates", info="Save intermediate checkpoints every N updates", minimum=10, ) keep_last_n_checkpoints = gr.Number( label="Keep Last N Checkpoints", step=1, precision=0, info="-1 to keep all, 0 to not save intermediate, > 0 to keep last N", minimum=-1, ) last_per_updates = gr.Number( label="Last per Updates", info="Save latest checkpoint with suffix _last.pt every N updates", minimum=10, ) gr.Radio(label="") # placeholder with gr.Row(): ch_8bit_adam = gr.Checkbox(label="Use 8-bit Adam optimizer") mixed_precision = gr.Radio(label="Mixed Precision", choices=["none", "fp16", "bf16"]) cd_logger = gr.Radio(label="Logger", choices=["none", "wandb", "tensorboard"]) with gr.Column(): start_button = gr.Button("Start Training") stop_button = gr.Button("Stop Training", interactive=False) if projects_selelect is not None: ( exp_name_value, learning_rate_value, batch_size_per_gpu_value, batch_size_type_value, max_samples_value, grad_accumulation_steps_value, max_grad_norm_value, epochs_value, num_warmup_updates_value, save_per_updates_value, keep_last_n_checkpoints_value, last_per_updates_value, finetune_value, file_checkpoint_train_value, tokenizer_type_value, tokenizer_file_value, mixed_precision_value, logger_value, bnb_optimizer_value, ) = load_settings(projects_selelect) # Assigning values to the respective components exp_name.value = exp_name_value learning_rate.value = learning_rate_value batch_size_per_gpu.value = batch_size_per_gpu_value batch_size_type.value = batch_size_type_value max_samples.value = max_samples_value grad_accumulation_steps.value = grad_accumulation_steps_value max_grad_norm.value = max_grad_norm_value epochs.value = epochs_value num_warmup_updates.value = num_warmup_updates_value save_per_updates.value = save_per_updates_value keep_last_n_checkpoints.value = keep_last_n_checkpoints_value last_per_updates.value = last_per_updates_value ch_finetune.value = finetune_value file_checkpoint_train.value = file_checkpoint_train_value tokenizer_type.value = tokenizer_type_value tokenizer_file.value = tokenizer_file_value mixed_precision.value = mixed_precision_value cd_logger.value = logger_value ch_8bit_adam.value = bnb_optimizer_value ch_stream = gr.Checkbox(label="Stream Output Experiment", value=True) txt_info_train = gr.Textbox(label="Info", value="") list_audios, select_audio = get_audio_project(projects_selelect, False) select_audio_ref = select_audio select_audio_gen = select_audio if select_audio is not None: select_audio_ref += "_ref.wav" select_audio_gen += "_gen.wav" with gr.Row(): ch_list_audio = gr.Dropdown( choices=list_audios, value=select_audio, label="Audios", allow_custom_value=True, scale=6, interactive=True, ) bt_stream_audio = gr.Button("Refresh", scale=1) bt_stream_audio.click(fn=get_audio_project, inputs=[cm_project], outputs=[ch_list_audio]) cm_project.change(fn=get_audio_project, inputs=[cm_project], outputs=[ch_list_audio]) with gr.Row(): audio_ref_stream = gr.Audio(label="Original", type="filepath", value=select_audio_ref) audio_gen_stream = gr.Audio(label="Generate", type="filepath", value=select_audio_gen) ch_list_audio.change( fn=get_audio_select, inputs=[ch_list_audio], outputs=[audio_ref_stream, audio_gen_stream], ) start_button.click( fn=start_training, inputs=[ cm_project, exp_name, learning_rate, batch_size_per_gpu, batch_size_type, max_samples, grad_accumulation_steps, max_grad_norm, epochs, num_warmup_updates, save_per_updates, keep_last_n_checkpoints, last_per_updates, ch_finetune, file_checkpoint_train, tokenizer_type, tokenizer_file, mixed_precision, ch_stream, cd_logger, ch_8bit_adam, ], outputs=[txt_info_train, start_button, stop_button], ) stop_button.click(fn=stop_training, outputs=[txt_info_train, start_button, stop_button]) bt_calculate.click( fn=calculate_train, inputs=[ cm_project, epochs, learning_rate, batch_size_per_gpu, batch_size_type, max_samples, num_warmup_updates, ch_finetune, ], outputs=[ epochs, learning_rate, batch_size_per_gpu, max_samples, num_warmup_updates, lb_samples, ], ) ch_finetune.change( check_finetune, inputs=[ch_finetune], outputs=[file_checkpoint_train, tokenizer_file, tokenizer_type] ) def setup_load_settings(): output_components = [ exp_name, learning_rate, batch_size_per_gpu, batch_size_type, max_samples, grad_accumulation_steps, max_grad_norm, epochs, num_warmup_updates, save_per_updates, keep_last_n_checkpoints, last_per_updates, ch_finetune, file_checkpoint_train, tokenizer_type, tokenizer_file, mixed_precision, cd_logger, ch_8bit_adam, ] return output_components outputs = setup_load_settings() cm_project.change( fn=load_settings, inputs=[cm_project], outputs=outputs, ) ch_refresh_project.click( fn=load_settings, inputs=[cm_project], outputs=outputs, ) with gr.TabItem("Test Model"): gr.Markdown("""```plaintext Check the use_ema setting (True or False) for your model to see what works best for you. Set seed to -1 for random. ```""") exp_name = gr.Radio( label="Model", choices=["F5TTS_v1_Base", "F5TTS_Base", "E2TTS_Base"], value="F5TTS_v1_Base" ) list_checkpoints, checkpoint_select = get_checkpoints_project(projects_selelect, False) with gr.Row(): nfe_step = gr.Number(label="NFE Step", value=32) speed = gr.Slider(label="Speed", value=1.0, minimum=0.3, maximum=2.0, step=0.1) seed = gr.Number(label="Random Seed", value=-1, minimum=-1) remove_silence = gr.Checkbox(label="Remove Silence") with gr.Row(): ch_use_ema = gr.Checkbox( label="Use EMA", value=True, info="Turn off at early stage might offer better results" ) cm_checkpoint = gr.Dropdown( choices=list_checkpoints, value=checkpoint_select, label="Checkpoints", allow_custom_value=True ) bt_checkpoint_refresh = gr.Button("Refresh") random_sample_infer = gr.Button("Random Sample") ref_text = gr.Textbox(label="Reference Text") ref_audio = gr.Audio(label="Reference Audio", type="filepath") gen_text = gr.Textbox(label="Text to Generate") random_sample_infer.click( fn=get_random_sample_infer, inputs=[cm_project], outputs=[ref_text, gen_text, ref_audio] ) with gr.Row(): txt_info_gpu = gr.Textbox("", label="Inference on Device :") seed_info = gr.Textbox(label="Used Random Seed :") check_button_infer = gr.Button("Inference") gen_audio = gr.Audio(label="Generated Audio", type="filepath") check_button_infer.click( fn=infer, inputs=[ cm_project, cm_checkpoint, exp_name, ref_text, ref_audio, gen_text, nfe_step, ch_use_ema, speed, seed, remove_silence, ], outputs=[gen_audio, txt_info_gpu, seed_info], ) bt_checkpoint_refresh.click(fn=get_checkpoints_project, inputs=[cm_project], outputs=[cm_checkpoint]) cm_project.change(fn=get_checkpoints_project, inputs=[cm_project], outputs=[cm_checkpoint]) with gr.TabItem("Prune Checkpoint"): gr.Markdown("""```plaintext Reduce the Base model size from 5GB to 1.3GB. The new checkpoint file prunes out optimizer and etc., can be used for inference or finetuning afterward, but not able to resume pretraining. ```""") txt_path_checkpoint = gr.Textbox(label="Path to Checkpoint:") txt_path_checkpoint_small = gr.Textbox(label="Path to Output:") with gr.Row(): ch_save_ema = gr.Checkbox(label="Save EMA checkpoint", value=True) ch_safetensors = gr.Checkbox(label="Save with safetensors format", value=True) txt_info_reduse = gr.Textbox(label="Info", value="") reduse_button = gr.Button("Prune") reduse_button.click( fn=prune_checkpoint, inputs=[txt_path_checkpoint, txt_path_checkpoint_small, ch_save_ema, ch_safetensors], outputs=[txt_info_reduse], ) with gr.TabItem("System Info"): output_box = gr.Textbox(label="GPU and CPU Information", lines=20) def update_stats(): return get_combined_stats() update_button = gr.Button("Update Stats") update_button.click(fn=update_stats, outputs=output_box) def auto_update(): yield gr.update(value=update_stats()) gr.update(fn=auto_update, inputs=[], outputs=output_box) @click.command() @click.option("--port", "-p", default=None, type=int, help="Port to run the app on") @click.option("--host", "-H", default=None, help="Host to run the app on") @click.option( "--share", "-s", default=False, is_flag=True, help="Share the app via Gradio share link", ) @click.option("--api", "-a", default=True, is_flag=True, help="Allow API access") def main(port, host, share, api): global app print("Starting app...") app.queue(api_open=api).launch(server_name=host, server_port=port, share=share, show_api=api) if __name__ == "__main__": main()