import os import re import torch import torchaudio import gradio as gr import numpy as np import tempfile from einops import rearrange from ema_pytorch import EMA from vocos import Vocos from pydub import AudioSegment from model import CFM, UNetT, DiT, MMDiT from cached_path import cached_path from model.utils import ( get_tokenizer, convert_char_to_pinyin, save_spectrogram, ) from transformers import pipeline import spaces import librosa device = "cuda" if torch.cuda.is_available() else "cpu" pipe = pipeline( "automatic-speech-recognition", model="openai/whisper-large-v3-turbo", torch_dtype=torch.float16, device=device, ) # --------------------- Settings -------------------- # target_sample_rate = 24000 n_mel_channels = 100 hop_length = 256 target_rms = 0.1 nfe_step = 32 # 16, 32 cfg_strength = 2.0 ode_method = 'euler' sway_sampling_coef = -1.0 speed = 1.0 # fix_duration = 27 # None or float (duration in seconds) fix_duration = None def load_model(exp_name, model_cls, model_cfg, ckpt_step): checkpoint = torch.load(str(cached_path(f"hf://SWivid/F5-TTS/{exp_name}/model_{ckpt_step}.pt")), map_location=device) vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin") model = CFM( transformer=model_cls( **model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels ), mel_spec_kwargs=dict( target_sample_rate=target_sample_rate, n_mel_channels=n_mel_channels, hop_length=hop_length, ), odeint_kwargs=dict( method=ode_method, ), vocab_char_map=vocab_char_map, ).to(device) ema_model = EMA(model, include_online_model=False).to(device) ema_model.load_state_dict(checkpoint['ema_model_state_dict']) ema_model.copy_params_from_ema_to_model() return ema_model, model # load models F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) F5TTS_ema_model, F5TTS_base_model = load_model("F5TTS_Base", DiT, F5TTS_model_cfg, 1200000) E2TTS_ema_model, E2TTS_base_model = load_model("E2TTS_Base", UNetT, E2TTS_model_cfg, 1200000) @spaces.GPU def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence): print(gen_text) if len(gen_text) > 200: raise gr.Error("Please keep your text under 200 chars.") gr.Info("Converting audio...") with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: aseg = AudioSegment.from_file(ref_audio_orig) audio_duration = len(aseg) if audio_duration > 15000: gr.Warning("Audio is over 15s, clipping to only first 15s.") aseg = aseg[:15000] aseg.export(f.name, format="wav") ref_audio = f.name if exp_name == "F5-TTS": ema_model = F5TTS_ema_model base_model = F5TTS_base_model elif exp_name == "E2-TTS": ema_model = E2TTS_ema_model base_model = E2TTS_base_model if not ref_text.strip(): gr.Info("No reference text provided, transcribing reference audio...") ref_text = outputs = pipe( ref_audio, chunk_length_s=30, batch_size=128, generate_kwargs={"task": "transcribe"}, return_timestamps=False, )['text'].strip() gr.Info("Finished transcription") else: gr.Info("Using custom reference text...") audio, sr = torchaudio.load(ref_audio) rms = torch.sqrt(torch.mean(torch.square(audio))) if rms < target_rms: audio = audio * target_rms / rms if sr != target_sample_rate: resampler = torchaudio.transforms.Resample(sr, target_sample_rate) audio = resampler(audio) audio = audio.to(device) # Prepare the text text_list = [ref_text + gen_text] final_text_list = convert_char_to_pinyin(text_list) # Calculate duration ref_audio_len = audio.shape[-1] // hop_length # if fix_duration is not None: # duration = int(fix_duration * target_sample_rate / hop_length) # else: zh_pause_punc = r"。，、；：？！" ref_text_len = len(ref_text) + len(re.findall(zh_pause_punc, ref_text)) gen_text_len = len(gen_text) + len(re.findall(zh_pause_punc, gen_text)) duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed) # inference gr.Info(f"Generating audio using {exp_name}") with torch.inference_mode(): generated, _ = base_model.sample( cond=audio, text=final_text_list, duration=duration, steps=nfe_step, cfg_strength=cfg_strength, sway_sampling_coef=sway_sampling_coef, ) generated = generated[:, ref_audio_len:, :] generated_mel_spec = rearrange(generated, '1 n d -> 1 d n') gr.Info("Running vocoder") vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") generated_wave = vocos.decode(generated_mel_spec.cpu()) if rms < target_rms: generated_wave = generated_wave * rms / target_rms # wav -> numpy generated_wave = generated_wave.squeeze().cpu().numpy() if remove_silence: gr.Info("Removing audio silences... This may take a moment") non_silent_intervals = librosa.effects.split(generated_wave, top_db=30) non_silent_wave = np.array([]) for interval in non_silent_intervals: start, end = interval non_silent_wave = np.concatenate([non_silent_wave, generated_wave[start:end]]) generated_wave = non_silent_wave # spectogram with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram: spectrogram_path = tmp_spectrogram.name save_spectrogram(generated_mel_spec[0].cpu().numpy(), spectrogram_path) return (target_sample_rate, generated_wave), spectrogram_path with gr.Blocks() as app: gr.Markdown(""" # E2/F5 TTS This is an unofficial E2/F5 TTS demo. This demo supports the following TTS models: * [E2-TTS](https://arxiv.org/abs/2406.18009) (Embarrassingly Easy Fully Non-Autoregressive Zero-Shot TTS) * [F5-TTS](https://arxiv.org/abs/2410.06885) (A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching) This demo is based on the [F5-TTS](https://github.com/SWivid/F5-TTS) codebase, which is based on an [unofficial E2-TTS implementation](https://github.com/lucidrains/e2-tts-pytorch). The checkpoints support English and Chinese. If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 15s, and shortening your prompt. If you're still running into issues, please open a [community Discussion](https://huggingface.co./spaces/mrfakename/E2-F5-TTS/discussions). Long-form/batched inference is coming soon! **NOTE: Reference text will be automatically transcribed with Whisper if not provided. For best results, keep your reference clips short (<15s). Ensure the audio is fully uploaded before generating.** """) ref_audio_input = gr.Audio(label="Reference Audio", type="filepath") gen_text_input = gr.Textbox(label="Text to Generate (max 200 chars.)", lines=4) model_choice = gr.Radio(choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS") generate_btn = gr.Button("Synthesize", variant="primary") with gr.Accordion("Advanced Settings", open=False): ref_text_input = gr.Textbox(label="Reference Text", info="Leave blank to automatically transcribe the reference audio. If you enter text it will override automatic transcription.", lines=2) remove_silence = gr.Checkbox(label="Remove Silences", info="The model tends to produce silences, especially on longer audio. We can manually remove silences if needed. Note that this is an experimental feature and may produce strange results. This will also increase generation time.", value=True) audio_output = gr.Audio(label="Synthesized Audio") spectrogram_output = gr.Image(label="Spectrogram") generate_btn.click(infer, inputs=[ref_audio_input, ref_text_input, gen_text_input, model_choice, remove_silence], outputs=[audio_output, spectrogram_output]) gr.Markdown(""" ## Run Locally Run this demo locally on CPU, CUDA, or MPS (Apple Silicon): First, ensure `ffmpeg` is installed. ```bash git clone https://huggingface.co./spaces/mrfakename/E2-F5-TTS cd E2-F5-TTS python -m pip install -r requirements.txt python app_local.py ``` """) gr.Markdown("Unofficial demo by [mrfakename](https://x.com/realmrfakename)") app.queue().launch()