import os import re import torch import torchaudio import gradio as gr import numpy as np import tempfile from einops import rearrange from vocos import Vocos from pydub import AudioSegment, silence from model import CFM, UNetT, DiT, MMDiT from cached_path import cached_path from model.utils import ( load_checkpoint, get_tokenizer, convert_char_to_pinyin, save_spectrogram, ) from transformers import pipeline import librosa import click import soundfile as sf try: import spaces USING_SPACES = True except ImportError: USING_SPACES = False def gpu_decorator(func): if USING_SPACES: return spaces.GPU(func) else: return func SPLIT_WORDS = [ "but", "however", "nevertheless", "yet", "still", "therefore", "thus", "hence", "consequently", "moreover", "furthermore", "additionally", "meanwhile", "alternatively", "otherwise", "namely", "specifically", "for example", "such as", "in fact", "indeed", "notably", "in contrast", "on the other hand", "conversely", "in conclusion", "to summarize", "finally" ] device = ( "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" ) print(f"Using {device} device") pipe = pipeline( "automatic-speech-recognition", model="openai/whisper-large-v3-turbo", torch_dtype=torch.float16, device=device, ) vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") # --------------------- 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(repo_name, exp_name, model_cls, model_cfg, ckpt_step): ckpt_path = str(cached_path(f"hf://SWivid/{repo_name}/{exp_name}/model_{ckpt_step}.safetensors")) # ckpt_path = f"ckpts/{exp_name}/model_{ckpt_step}.pt" # .pt | .safetensors 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) model = load_checkpoint(model, ckpt_path, device, use_ema = True) return 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 = load_model( "F5-TTS", "F5TTS_Base", DiT, F5TTS_model_cfg, 1200000 ) E2TTS_ema_model = load_model( "E2-TTS", "E2TTS_Base", UNetT, E2TTS_model_cfg, 1200000 ) def split_text_into_batches(text, max_chars=200, split_words=SPLIT_WORDS): if len(text.encode('utf-8')) <= max_chars: return [text] if text[-1] not in ['。', '.', '!', '！', '?', '？']: text += '.' sentences = re.split('([。.!?！？])', text) sentences = [''.join(i) for i in zip(sentences[0::2], sentences[1::2])] batches = [] current_batch = "" def split_by_words(text): words = text.split() current_word_part = "" word_batches = [] for word in words: if len(current_word_part.encode('utf-8')) + len(word.encode('utf-8')) + 1 <= max_chars: current_word_part += word + ' ' else: if current_word_part: # Try to find a suitable split word for split_word in split_words: split_index = current_word_part.rfind(' ' + split_word + ' ') if split_index != -1: word_batches.append(current_word_part[:split_index].strip()) current_word_part = current_word_part[split_index:].strip() + ' ' break else: # If no suitable split word found, just append the current part word_batches.append(current_word_part.strip()) current_word_part = "" current_word_part += word + ' ' if current_word_part: word_batches.append(current_word_part.strip()) return word_batches for sentence in sentences: if len(current_batch.encode('utf-8')) + len(sentence.encode('utf-8')) <= max_chars: current_batch += sentence else: # If adding this sentence would exceed the limit if current_batch: batches.append(current_batch) current_batch = "" # If the sentence itself is longer than max_chars, split it if len(sentence.encode('utf-8')) > max_chars: # First, try to split by colon colon_parts = sentence.split(':') if len(colon_parts) > 1: for part in colon_parts: if len(part.encode('utf-8')) <= max_chars: batches.append(part) else: # If colon part is still too long, split by comma comma_parts = re.split('[,，]', part) if len(comma_parts) > 1: current_comma_part = "" for comma_part in comma_parts: if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars: current_comma_part += comma_part + ',' else: if current_comma_part: batches.append(current_comma_part.rstrip(',')) current_comma_part = comma_part + ',' if current_comma_part: batches.append(current_comma_part.rstrip(',')) else: # If no comma, split by words batches.extend(split_by_words(part)) else: # If no colon, split by comma comma_parts = re.split('[,，]', sentence) if len(comma_parts) > 1: current_comma_part = "" for comma_part in comma_parts: if len(current_comma_part.encode('utf-8')) + len(comma_part.encode('utf-8')) <= max_chars: current_comma_part += comma_part + ',' else: if current_comma_part: batches.append(current_comma_part.rstrip(',')) current_comma_part = comma_part + ',' if current_comma_part: batches.append(current_comma_part.rstrip(',')) else: # If no comma, split by words batches.extend(split_by_words(sentence)) else: current_batch = sentence if current_batch: batches.append(current_batch) return batches @spaces.GPU def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence, progress=gr.Progress()): if exp_name == "F5-TTS": ema_model = F5TTS_ema_model elif exp_name == "E2-TTS": ema_model = E2TTS_ema_model audio, sr = ref_audio if audio.shape[0] > 1: audio = torch.mean(audio, dim=0, keepdim=True) 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) generated_waves = [] spectrograms = [] for i, gen_text in enumerate(progress.tqdm(gen_text_batches)): # Prepare the text if len(ref_text[-1].encode('utf-8')) == 1: ref_text = ref_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 zh_pause_punc = r"。，、；：？！" ref_text_len = len(ref_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, ref_text)) gen_text_len = len(gen_text.encode('utf-8')) + 3 * len(re.findall(zh_pause_punc, gen_text)) duration = ref_audio_len + int(ref_audio_len / ref_text_len * gen_text_len / speed) # inference with torch.inference_mode(): generated, _ = ema_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") 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() generated_waves.append(generated_wave) spectrograms.append(generated_mel_spec[0].cpu().numpy()) # Combine all generated waves final_wave = np.concatenate(generated_waves) # Remove silence if remove_silence: with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: sf.write(f.name, final_wave, target_sample_rate) aseg = AudioSegment.from_file(f.name) non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500) non_silent_wave = AudioSegment.silent(duration=0) for non_silent_seg in non_silent_segs: non_silent_wave += non_silent_seg aseg = non_silent_wave aseg.export(f.name, format="wav") final_wave, _ = torchaudio.load(f.name) final_wave = final_wave.squeeze().cpu().numpy() # Create a combined spectrogram combined_spectrogram = np.concatenate(spectrograms, axis=1) with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_spectrogram: spectrogram_path = tmp_spectrogram.name save_spectrogram(combined_spectrogram, spectrogram_path) return (target_sample_rate, final_wave), spectrogram_path @spaces.GPU def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, custom_split_words=''): if not custom_split_words.strip(): custom_words = [word.strip() for word in custom_split_words.split(',')] global SPLIT_WORDS SPLIT_WORDS = custom_words print(gen_text) gr.Info("Converting audio...") with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: aseg = AudioSegment.from_file(ref_audio_orig) non_silent_segs = silence.split_on_silence(aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=500) non_silent_wave = AudioSegment.silent(duration=0) for non_silent_seg in non_silent_segs: non_silent_wave += non_silent_seg aseg = non_silent_wave 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 not ref_text.strip(): gr.Info("No reference text provided, transcribing reference audio...") ref_text = 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...") # Split the input text into batches audio, sr = torchaudio.load(ref_audio) max_chars = int(len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (30 - audio.shape[-1] / sr)) gen_text_batches = split_text_into_batches(gen_text, max_chars=max_chars) print('ref_text', ref_text) for i, gen_text in enumerate(gen_text_batches): print(f'gen_text {i}', gen_text) gr.Info(f"Generating audio using {exp_name} in {len(gen_text_batches)} batches") return infer_batch((audio, sr), ref_text, gen_text_batches, exp_name, remove_silence) @spaces.GPU def generate_podcast(script, speaker1_name, ref_audio1, ref_text1, speaker2_name, ref_audio2, ref_text2, exp_name, remove_silence): # Split the script into speaker blocks speaker_pattern = re.compile(f"^({re.escape(speaker1_name)}|{re.escape(speaker2_name)}):", re.MULTILINE) speaker_blocks = speaker_pattern.split(script)[1:] # Skip the first empty element generated_audio_segments = [] for i in range(0, len(speaker_blocks), 2): speaker = speaker_blocks[i] text = speaker_blocks[i+1].strip() # Determine which speaker is talking if speaker == speaker1_name: ref_audio = ref_audio1 ref_text = ref_text1 elif speaker == speaker2_name: ref_audio = ref_audio2 ref_text = ref_text2 else: continue # Skip if the speaker is neither speaker1 nor speaker2 # Generate audio for this block audio, _ = infer(ref_audio, ref_text, text, exp_name, remove_silence) # Convert the generated audio to a numpy array sr, audio_data = audio # Save the audio data as a WAV file with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: sf.write(temp_file.name, audio_data, sr) audio_segment = AudioSegment.from_wav(temp_file.name) generated_audio_segments.append(audio_segment) # Add a short pause between speakers pause = AudioSegment.silent(duration=500) # 500ms pause generated_audio_segments.append(pause) # Concatenate all audio segments final_podcast = sum(generated_audio_segments) # Export the final podcast with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_file: podcast_path = temp_file.name final_podcast.export(podcast_path, format="wav") return podcast_path def parse_speechtypes_text(gen_text): # Pattern to find (Emotion) pattern = r'\((.*?)\)' # Split the text by the pattern tokens = re.split(pattern, gen_text) segments = [] current_emotion = 'Regular' for i in range(len(tokens)): if i % 2 == 0: # This is text text = tokens[i].strip() if text: segments.append({'emotion': current_emotion, 'text': text}) else: # This is emotion emotion = tokens[i].strip() current_emotion = emotion return segments def update_speed(new_speed): global speed speed = new_speed return f"Speed set to: {speed}" with gr.Blocks() as app_credits: gr.Markdown(""" # Credits * [mrfakename](https://github.com/fakerybakery) for the original [online demo](https://huggingface.co/spaces/mrfakename/E2-F5-TTS) * [RootingInLoad](https://github.com/RootingInLoad) for the podcast generation """) with gr.Blocks() as app_tts: gr.Markdown("# Batched TTS") ref_audio_input = gr.Audio(label="Reference Audio", type="filepath") gen_text_input = gr.Textbox(label="Text to Generate", lines=10) 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, ) split_words_input = gr.Textbox( label="Custom Split Words", info="Enter custom words to split on, separated by commas. Leave blank to use default list.", lines=2, ) speed_slider = gr.Slider( label="Speed", minimum=0.3, maximum=2.0, value=speed, step=0.1, info="Adjust the speed of the audio.", ) speed_slider.change(update_speed, inputs=speed_slider) 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, split_words_input, ], outputs=[audio_output, spectrogram_output], ) with gr.Blocks() as app_podcast: gr.Markdown("# Podcast Generation") speaker1_name = gr.Textbox(label="Speaker 1 Name") ref_audio_input1 = gr.Audio(label="Reference Audio (Speaker 1)", type="filepath") ref_text_input1 = gr.Textbox(label="Reference Text (Speaker 1)", lines=2) speaker2_name = gr.Textbox(label="Speaker 2 Name") ref_audio_input2 = gr.Audio(label="Reference Audio (Speaker 2)", type="filepath") ref_text_input2 = gr.Textbox(label="Reference Text (Speaker 2)", lines=2) script_input = gr.Textbox(label="Podcast Script", lines=10, placeholder="Enter the script with speaker names at the start of each block, e.g.:\nSean: How did you start studying...\n\nMeghan: I came to my interest in technology...\nIt was a long journey...\n\nSean: That's fascinating. Can you elaborate...") podcast_model_choice = gr.Radio( choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS" ) podcast_remove_silence = gr.Checkbox( label="Remove Silences", value=True, ) generate_podcast_btn = gr.Button("Generate Podcast", variant="primary") podcast_output = gr.Audio(label="Generated Podcast") def podcast_generation(script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence): return generate_podcast(script, speaker1, ref_audio1, ref_text1, speaker2, ref_audio2, ref_text2, model, remove_silence) generate_podcast_btn.click( podcast_generation, inputs=[ script_input, speaker1_name, ref_audio_input1, ref_text_input1, speaker2_name, ref_audio_input2, ref_text_input2, podcast_model_choice, podcast_remove_silence, ], outputs=podcast_output, ) def parse_emotional_text(gen_text): # Pattern to find (Emotion) pattern = r'\((.*?)\)' # Split the text by the pattern tokens = re.split(pattern, gen_text) segments = [] current_emotion = 'Regular' for i in range(len(tokens)): if i % 2 == 0: # This is text text = tokens[i].strip() if text: segments.append({'emotion': current_emotion, 'text': text}) else: # This is emotion emotion = tokens[i].strip() current_emotion = emotion return segments with gr.Blocks() as app_emotional: # New section for emotional generation gr.Markdown( """ # Multiple Speech-Type Generation This section allows you to upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the "Add Speech Type" button. Enter your text in the format shown below, and the system will generate speech using the appropriate emotions. If unspecified, the model will use the regular speech type. The current speech type will be used until the next speech type is specified. **Example Input:** (Regular) Hello, I'd like to order a sandwich please. (Surprised) What do you mean you're out of bread? (Sad) I really wanted a sandwich though... (Angry) You know what, darn you and your little shop, you suck! (Whisper) I'll just go back home and cry now. (Shouting) Why me?! """ ) gr.Markdown("Upload different audio clips for each speech type. 'Regular' emotion is mandatory. You can add additional speech types by clicking the 'Add Speech Type' button.") # Regular speech type (mandatory) with gr.Row(): regular_name = gr.Textbox(value='Regular', label='Speech Type Name', interactive=False) regular_audio = gr.Audio(label='Regular Reference Audio', type='filepath') regular_ref_text = gr.Textbox(label='Reference Text (Regular)', lines=2) # Additional speech types (up to 9 more) max_speech_types = 10 speech_type_names = [] speech_type_audios = [] speech_type_ref_texts = [] speech_type_delete_btns = [] for i in range(max_speech_types - 1): with gr.Row(): name_input = gr.Textbox(label='Speech Type Name', visible=False) audio_input = gr.Audio(label='Reference Audio', type='filepath', visible=False) ref_text_input = gr.Textbox(label='Reference Text', lines=2, visible=False) delete_btn = gr.Button("Delete", variant="secondary", visible=False) speech_type_names.append(name_input) speech_type_audios.append(audio_input) speech_type_ref_texts.append(ref_text_input) speech_type_delete_btns.append(delete_btn) # Button to add speech type add_speech_type_btn = gr.Button("Add Speech Type") # Keep track of current number of speech types speech_type_count = gr.State(value=0) # Function to add a speech type def add_speech_type_fn(speech_type_count): if speech_type_count < max_speech_types - 1: speech_type_count += 1 # Prepare updates for the components name_updates = [] audio_updates = [] ref_text_updates = [] delete_btn_updates = [] for i in range(max_speech_types - 1): if i < speech_type_count: name_updates.append(gr.update(visible=True)) audio_updates.append(gr.update(visible=True)) ref_text_updates.append(gr.update(visible=True)) delete_btn_updates.append(gr.update(visible=True)) else: name_updates.append(gr.update()) audio_updates.append(gr.update()) ref_text_updates.append(gr.update()) delete_btn_updates.append(gr.update()) else: # Optionally, show a warning # gr.Warning("Maximum number of speech types reached.") name_updates = [gr.update() for _ in range(max_speech_types - 1)] audio_updates = [gr.update() for _ in range(max_speech_types - 1)] ref_text_updates = [gr.update() for _ in range(max_speech_types - 1)] delete_btn_updates = [gr.update() for _ in range(max_speech_types - 1)] return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates add_speech_type_btn.click( add_speech_type_fn, inputs=speech_type_count, outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns ) # Function to delete a speech type def make_delete_speech_type_fn(index): def delete_speech_type_fn(speech_type_count): # Prepare updates name_updates = [] audio_updates = [] ref_text_updates = [] delete_btn_updates = [] for i in range(max_speech_types - 1): if i == index: name_updates.append(gr.update(visible=False, value='')) audio_updates.append(gr.update(visible=False, value=None)) ref_text_updates.append(gr.update(visible=False, value='')) delete_btn_updates.append(gr.update(visible=False)) else: name_updates.append(gr.update()) audio_updates.append(gr.update()) ref_text_updates.append(gr.update()) delete_btn_updates.append(gr.update()) speech_type_count = max(0, speech_type_count - 1) return [speech_type_count] + name_updates + audio_updates + ref_text_updates + delete_btn_updates return delete_speech_type_fn for i, delete_btn in enumerate(speech_type_delete_btns): delete_fn = make_delete_speech_type_fn(i) delete_btn.click( delete_fn, inputs=speech_type_count, outputs=[speech_type_count] + speech_type_names + speech_type_audios + speech_type_ref_texts + speech_type_delete_btns ) # Text input for the prompt gen_text_input_emotional = gr.Textbox(label="Text to Generate", lines=10) # Model choice model_choice_emotional = gr.Radio( choices=["F5-TTS", "E2-TTS"], label="Choose TTS Model", value="F5-TTS" ) with gr.Accordion("Advanced Settings", open=False): remove_silence_emotional = gr.Checkbox( label="Remove Silences", value=True, ) # Generate button generate_emotional_btn = gr.Button("Generate Emotional Speech", variant="primary") # Output audio audio_output_emotional = gr.Audio(label="Synthesized Audio") @spaces.GPU def generate_emotional_speech( regular_audio, regular_ref_text, gen_text, *args, ): num_additional_speech_types = max_speech_types - 1 speech_type_names_list = args[:num_additional_speech_types] speech_type_audios_list = args[num_additional_speech_types:2 * num_additional_speech_types] speech_type_ref_texts_list = args[2 * num_additional_speech_types:3 * num_additional_speech_types] model_choice = args[3 * num_additional_speech_types] remove_silence = args[3 * num_additional_speech_types + 1] # Collect the speech types and their audios into a dict speech_types = {'Regular': {'audio': regular_audio, 'ref_text': regular_ref_text}} for name_input, audio_input, ref_text_input in zip(speech_type_names_list, speech_type_audios_list, speech_type_ref_texts_list): if name_input and audio_input: speech_types[name_input] = {'audio': audio_input, 'ref_text': ref_text_input} # Parse the gen_text into segments segments = parse_speechtypes_text(gen_text) # For each segment, generate speech generated_audio_segments = [] current_emotion = 'Regular' for segment in segments: emotion = segment['emotion'] text = segment['text'] if emotion in speech_types: current_emotion = emotion else: # If emotion not available, default to Regular current_emotion = 'Regular' ref_audio = speech_types[current_emotion]['audio'] ref_text = speech_types[current_emotion].get('ref_text', '') # Generate speech for this segment audio, _ = infer(ref_audio, ref_text, text, model_choice, remove_silence, "") sr, audio_data = audio generated_audio_segments.append(audio_data) # Concatenate all audio segments if generated_audio_segments: final_audio_data = np.concatenate(generated_audio_segments) return (sr, final_audio_data) else: gr.Warning("No audio generated.") return None generate_emotional_btn.click( generate_emotional_speech, inputs=[ regular_audio, regular_ref_text, gen_text_input_emotional, ] + speech_type_names + speech_type_audios + speech_type_ref_texts + [ model_choice_emotional, remove_silence_emotional, ], outputs=audio_output_emotional, ) # Validation function to disable Generate button if speech types are missing def validate_speech_types( gen_text, regular_name, *args ): num_additional_speech_types = max_speech_types - 1 speech_type_names_list = args[:num_additional_speech_types] # Collect the speech types names speech_types_available = set() if regular_name: speech_types_available.add(regular_name) for name_input in speech_type_names_list: if name_input: speech_types_available.add(name_input) # Parse the gen_text to get the speech types used segments = parse_emotional_text(gen_text) speech_types_in_text = set(segment['emotion'] for segment in segments) # Check if all speech types in text are available missing_speech_types = speech_types_in_text - speech_types_available if missing_speech_types: # Disable the generate button return gr.update(interactive=False) else: # Enable the generate button return gr.update(interactive=True) gen_text_input_emotional.change( validate_speech_types, inputs=[gen_text_input_emotional, regular_name] + speech_type_names, outputs=generate_emotional_btn ) with gr.Blocks() as app: gr.Markdown( """ # E2/F5 TTS This is a local web UI for F5 TTS with advanced batch processing 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 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. **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.** """ ) gr.TabbedInterface([app_tts, app_podcast, app_emotional, app_credits], ["TTS", "Podcast", "Multi-Style", "Credits"]) app.queue().launch()