# Import 'spaces' early to prevent CUDA initialization conflicts try: import spaces USING_SPACES = True except ImportError: USING_SPACES = False # Delay PyTorch and related imports until after 'spaces' import re import gradio as gr import numpy as np import tempfile from tqdm import tqdm from einops import rearrange from pydub import AudioSegment, silence from model import UNetT, DiT from cached_path import cached_path from model.utils import ( get_tokenizer, convert_char_to_pinyin, ) from infer.utils_infer import ( load_vocoder, load_model, # preprocess_ref_audio_text, # infer_process, remove_silence_edges, remove_silence_for_generated_wav, save_spectrogram, ) from tokenizers import Tokenizer from phonemizer import phonemize from transformers import pipeline import click import soundfile as sf # Import PyTorch and torchaudio after 'spaces' import torch import torchaudio # GPU decorator for 'spaces' def gpu_decorator(func): if USING_SPACES: return spaces.GPU(func) else: return func # Determine the device device = ( "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" ) # Set dtype: float16 for GPU, bfloat16 for CPU, and default to float32 for other cases if device == "cuda": dtype = torch.float16 elif device == "cpu": dtype = torch.float32 else: dtype = torch.float32 # Create the torch.device object device = torch.device(device) print(f"Using device: {device}, dtype: {dtype}") pipe = pipeline( "automatic-speech-recognition", model="openai/whisper-large-v3-turbo", torch_dtype=dtype, device=device, ) #vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz") vocos = load_vocoder() # --------------------- Settings -------------------- # target_sample_rate = 24000 n_mel_channels = 100 hop_length = 256 target_rms = 0.1 nfe_step = 16 # 16, 32 cfg_strength = 2.0 ode_method = "euler" sway_sampling_coef = -1.0 speed = 1 fix_duration = None ref_language = "en-us" language = "en-us" DEFAULT_TTS_MODEL = "F5-TTS" tts_model_choice = DEFAULT_TTS_MODEL # load models #def load_f5tts(ckpt_path=str(cached_path("hf://SWivid/F5-TTS/F5TTS_Base/model_1200000.safetensors"))): # F5TTS_model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) # return load_model(DiT, F5TTS_model_cfg, ckpt_path) #def load_e2tts(ckpt_path=str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))): # E2TTS_model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4) # return load_model(UNetT, E2TTS_model_cfg, ckpt_path) def load_custom(ckpt_path: str, vocab_path="", model_cfg=None): ckpt_path, vocab_path = ckpt_path.strip(), vocab_path.strip() if ckpt_path.startswith("hf://"): ckpt_path = str(cached_path(ckpt_path)) if vocab_path.startswith("hf://"): vocab_path = str(cached_path(vocab_path)) if model_cfg is None: model_cfg = dict(dim=1024, depth=22, heads=16, ff_mult=2, text_dim=512, conv_layers=4) return load_model(DiT, model_cfg, ckpt_path, vocab_file=vocab_path) #F2TTS_ema_model3 = load_f5tts() #E2TTS_ema_model4 = load_e2tts() if USING_SPACES else None custom_ema_model, pre_custom_path = None, "" chat_model_state = None chat_tokenizer_state = None # 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_custom( # "hf://Gregniuki/F5-tts_English_German_Polish/English/model_222600.pt", "", F5TTS_model_cfg #) F5TTS_ema_model = load_custom( "hf://Gregniuki/F5-tts_English_German_Polish/multi/model_300000.pt", "", F5TTS_model_cfg ) #E2TTS_ema_model2 = load_custom( # "hf://Gregniuki/F5-tts_English_German_Polish/Polish/model_500000.pt", "", F5TTS_model_cfg #) def chunk_text(text, max_chars): """ Splits the input text into chunks, ensuring: - Chunks are split by punctuation where possible. - If no punctuation is found and the chunk exceeds `split_after_space_chars`, it is split into smaller chunks of up to `split_after_space_chars`. Args: text (str): The text to be split. max_chars (int): The maximum number of characters per chunk after punctuation. split_after_space_chars (int): The maximum number of characters per chunk when no punctuation is present. Returns: List[str]: A list of text chunks. """ if max_chars > 135: max_chars = 135 if max_chars < 50: max_chars = 50 split_after_space_chars = max_chars + int(max_chars * 0.33) chunks = [] current_chunk = "" # Split the text into sentences based on punctuation followed by whitespace sentences = re.split(r"(?<=[;:,.!?])\s+|(?<=[；：，。！？])", text) for sentence in sentences: # If adding this sentence doesn't exceed max_chars, append it to the current chunk if len(current_chunk) + len(sentence) + 1 <= max_chars: # +1 for space current_chunk += sentence + " " else: # If current chunk exceeds split_after_space_chars, handle the splitting while len(current_chunk) > split_after_space_chars: split_index = current_chunk.rfind(" ", 0, split_after_space_chars) if split_index == -1: # No spaces to split; force split at 135 characters split_index = split_after_space_chars chunks.append(current_chunk[:split_index].strip()) current_chunk = current_chunk[split_index:].strip() # Add the current chunk to the list and start a new chunk if current_chunk: chunks.append(current_chunk.strip()) current_chunk = sentence + " " # If the remaining chunk exceeds split_after_space_chars, split it further while len(current_chunk) > split_after_space_chars: split_index = current_chunk.rfind(" ", 0, split_after_space_chars) if split_index == -1: # No spaces to split; force split at 135 characters split_index = split_after_space_chars chunks.append(current_chunk[:split_index].strip()) current_chunk = current_chunk[split_index:].strip() # Add any leftover chunk if current_chunk: chunks.append(current_chunk.strip()) return chunks def text_to_ipa(text, language=language): try: ipa_text = phonemize( text, language=language, backend='espeak', strip=False, preserve_punctuation=True, with_stress=True ) return ipa_text #preserve_case(text, ipa_text) except Exception as e: print(f"Error processing text: {text}. Error: {e}") return None @gpu_decorator def infer_batch(ref_audio, ref_text, gen_text_batches, exp_name, remove_silence, cross_fade_duration=0.15, progress=gr.Progress()): if exp_name == "Multi": ema_model = F5TTS_ema_model # elif exp_name == "Polish": # ema_model = E2TTS_ema_model # elif exp_name == "Deutsch": # ema_model = E2TTS_ema_model2 #ref_audio, ref_text = preprocess_ref_audio_text(ref_audio, ref_text, show_info=show_info) 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) tokenizer = Tokenizer.from_file("data/Emilia_ZH_EN_pinyin/tokenizer.json") vocab_size = tokenizer.get_vocab_size() vocab = tokenizer.get_vocab() generated_waves = [] spectrograms = [] # Remove the last 5 chats, ensuring you don't attempt to slice beyond the list length ref_text = ref_text[:-5] + "... " if len(ref_text) >= 5 else ref_text # Ensure ref_text ends with a space if the last character is single-byte # if len(ref_text[-1].encode("utf-8")) == 1: # ref_text = ref_text + ". ." # Define weights for characters punctuation_weights = {",": 0, ".": 0, " ": 0} # Add more punctuation as needed progress = tqdm(gen_text_batches) ipa_text_ref = text_to_ipa(ref_text, language=ref_language) print(ref_language) print(language) for i, gen_text in enumerate(progress): # for i, gen_text in enumerate(progress.tqdm(gen_text_batches)): # Prepare the text ipa_text_gen = text_to_ipa(gen_text, language=language) print(ipa_text_gen) text_list = ipa_text_ref + ipa_text_gen print(text_list) encoding = tokenizer.encode(text_list) tokens = encoding.tokens text_list = ' '.join(map(str, tokens)) final_text_list = [text_list] print(final_text_list) # Calculate reference audio length ref_audio_len = audio.shape[-1] // hop_length if fix_duration is not None: duration = int(fix_duration * target_sample_rate / hop_length) else: # Calculate text lengths with weights def calculate_weighted_length(text): length = len(text.encode("utf-8")) additional_length = sum(punctuation_weights.get(char, 0) for char in text) return length + additional_length ref_text_len = calculate_weighted_length(ref_text) gen_text_len = calculate_weighted_length(gen_text) # Duration calculation considering global speed factor # duration = int(ref_audio_len) + int(((ref_audio_len / ref_text_len) * gen_text_len) / speed) duration = max(250, int(ref_audio_len) + int(((ref_audio_len / ref_text_len) * gen_text_len) / speed)) # Print the calculated duration print(f"Chunk {i + 1}: Duration: {duration} speed {speed}") # inference with torch.inference_mode(): # Ensure all inputs are on the same device as ema_model audio = audio.to(ema_model.device) # Match ema_model's device final_text_list = [t.to(ema_model.device) if isinstance(t, torch.Tensor) else t for t in final_text_list] 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, ) # Process generated tensor generated = generated[:, ref_audio_len:, :] generated_mel_spec = rearrange(generated, "1 n d -> 1 d n") # Convert to appropriate dtype and device # generated_mel_spec = generated_mel_spec.to(dtype=torch.float16, device=vocos.device) # Ensure device matches vocos generated_wave = vocos.decode(generated_mel_spec) # Adjust wave RMS if needed if rms < target_rms: generated_wave = generated_wave * rms / target_rms # Convert to numpy generated_wave = generated_wave.squeeze().cpu().numpy() # Append to list generated_waves.append(generated_wave) # spectrograms.append(generated_mel_spec[0].cpu().numpy()) # Ensure generated_mel_spec is in a compatible dtype (e.g., float32) before passing it to numpy # generated_mel_spec = generated_mel_spec.to(dtype=torch.float32) # Convert to float32 if it's in bfloat16 # Proceed with the rest of your operations spectrograms.append(generated_mel_spec[0].cpu().numpy()) # Combine all generated waves with cross-fading if cross_fade_duration <= 0: # Simply concatenate final_wave = np.concatenate(generated_waves) else: final_wave = generated_waves[0] for i in range(1, len(generated_waves)): prev_wave = final_wave next_wave = generated_waves[i] # Calculate cross-fade samples, ensuring it does not exceed wave lengths cross_fade_samples = int(cross_fade_duration * target_sample_rate) cross_fade_samples = min(cross_fade_samples, len(prev_wave), len(next_wave)) if cross_fade_samples <= 0: # No overlap possible, concatenate final_wave = np.concatenate([prev_wave, next_wave]) continue # Overlapping parts prev_overlap = prev_wave[-cross_fade_samples:] next_overlap = next_wave[:cross_fade_samples] # Fade out and fade in fade_out = np.linspace(1, 0, cross_fade_samples) fade_in = np.linspace(0, 1, cross_fade_samples) # Cross-faded overlap cross_faded_overlap = prev_overlap * fade_out + next_overlap * fade_in # Combine new_wave = np.concatenate([ prev_wave[:-cross_fade_samples], cross_faded_overlap, next_wave[cross_fade_samples:] ]) final_wave = new_wave # Remove silence if remove_silence: with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: # Convert to float32 before writing final_wave_float32 = final_wave.astype(np.float32) sf.write(f.name, final_wave_float32, 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 @gpu_decorator def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, cross_fade_duration=0.15 # Set the desired language code dynamically ): print(gen_text) gr.Info("Converting audio...") with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f: aseg = AudioSegment.from_file(ref_audio_orig) aseg = remove_silence_edges(aseg) + AudioSegment.silent(duration=150) non_silent_segs = silence.split_on_silence( aseg, min_silence_len=700, silence_thresh=-50, keep_silence=700 ) 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 > 10000: gr.Warning("Audio is over 10s, clipping to only first 10s.") aseg = aseg[:10000] 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=15, batch_size=128, generate_kwargs={"task": "transcribe"# ,"language": ref_language # Use the variable here }, return_timestamps=False, )["text"].strip() gr.Info("Finished transcription") else: gr.Info("Using custom reference text...") # Add the functionality to ensure it ends with ". " if not ref_text.endswith(". "): if ref_text.endswith("."): ref_text += " " else: ref_text += ". " audio, sr = torchaudio.load(ref_audio) # Use the new chunk_text function to split gen_text max_chars = int(speed * 0.5 * (len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (20 - audio.shape[-1] / sr ))) print(f"text: {max_chars} ") gen_text_batches = chunk_text(gen_text, max_chars=max_chars) print('ref_text', ref_text) for i, batch_text in enumerate(gen_text_batches): print(f'gen_text {i}', batch_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, cross_fade_duration) @gpu_decorator 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 # Function to update language def update_language(new_language): global language language = new_language return f"Language set to: {language}" def update_language1(new_ref_language): global ref_language ref_language = new_ref_language return f"Language set to: {ref_language}" 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 * [jpgallegoar](https://github.com/jpgallegoar) for multiple speech-type 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=["Multi"], label="Choose TTS Model", value="Multi" ) gr.Markdown("#Select Reference Language") language_choice1 = gr.Dropdown( choices=["pl", "de", "en-us", "en-gb", "uk", "ru", "cs", # Czech "sk", # Slovak "bg", # Bulgarian "sr", # Serbian "hr", # Croatian "sl", # Slovenian "be", # Belarusian "lt", # Lithuanian "lv", # Latvian "et", # Estonian "fi", # Finnish "hu", # Hungarian "sv", # Swedish "no", # Norwegian "da", # Danish "is", # Icelandic "nl" # Dutch ], label="Choose Language", value="en-us" ) gr.Markdown("#Select Synthesized Language") language_choice = gr.Dropdown( choices=["pl", "de", "en-us", "en-gb", "uk", "ru", "cs", # Czech "sk", # Slovak "bg", # Bulgarian "sr", # Serbian "hr", # Croatian "sl", # Slovenian "be", # Belarusian "lt", # Lithuanian "lv", # Latvian "et", # Estonian "fi", # Finnish "hu", # Hungarian "sv", # Swedish "no", # Norwegian "da", # Danish "is", # Icelandic "nl" # Dutch ], label="Choose Language", value="en-us" ) 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=False, ) speed_slider = gr.Slider( label="Speed", minimum=0.3, maximum=2.0, value=1.0, # Assuming a default speed value step=0.1, info="Adjust the speed of the audio.", ) cross_fade_duration_slider = gr.Slider( label="Cross-Fade Duration (s)", minimum=0.0, maximum=1.0, value=0.15, step=0.01, info="Set the duration of the cross-fade between audio clips.", ) speed_slider.change(update_speed, inputs=speed_slider) language_choice.change(update_language, inputs=language_choice) language_choice1.change(update_language1, inputs=language_choice1) 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, cross_fade_duration_slider, # language_choice, ], outputs=[audio_output, spectrogram_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=["Multi"], label="Choose TTS Model", value="Multi" ) 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") @gpu_decorator 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) # Ensure audio_data is float32 #audio_data = audio_data.astype(np.float32) generated_audio_segments.append(audio_data) # Concatenate all audio segments if generated_audio_segments: final_audio_data = np.concatenate(generated_audio_segments)#.astype(np.float32) 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( """ # 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 checkpoint support Polish English and German. Generations using CPU takes usually 2-3 minutes using 8 step inferece. If you're having issues, try converting your reference audio to WAV or MP3, clipping it to 5s, 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.HTML( """ Buy Me A Coffee """ ) gr.TabbedInterface([app_tts, app_emotional, app_credits], ["TTS", "Multi-Style", "Credits"]) @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(f"Starting app...") app.queue(api_open=api).launch( server_name=host, server_port=port, share=share, show_api=api ) if __name__ == "__main__": main()