Modified Interface

app.py

@@ -1,327 +1,50 @@
-# 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
-# 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
+# Gradio Application for Voice Cloning
+# Version as of 21/10/2024
 
-# 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,
-# )
-
-# # --------------------- 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):
-#     ckpt_path = str(cached_path(f"hf://SWivid/F5-TTS/{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(
-#     "F5TTS_Base", DiT, F5TTS_model_cfg, 1200000
-# )
-# E2TTS_ema_model = load_model(
-#     "E2TTS_Base", UNetT, E2TTS_model_cfg, 1200000
-# )
-
-
-# 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
-#     elif exp_name == "E2-TTS":
-#         ema_model = E2TTS_ema_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)
-#     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)
-
-#     # 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, _ = 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")
-#     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(
-#         """
-# # Antriksh AI
-
-# """
-#     )
-    
-#     # Image
-#     gr.Image(value="C:\\Users\\USER\\OneDrive\\Documents\\logo.jpg", width=300, height= 150 )
-
-#     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],
-#     )
-
-
-# @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=True,
-#     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=True, show_api=api
-#     )
-
-
-# if __name__ == "__main__":
-#     main()
-
-import re
+import gradio as gr
 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 model import CFM, UNetT
 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 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
-
-device = (
-    "cuda"
-    if torch.cuda.is_available()
-    else "mps" if torch.backends.mps.is_available() else "cpu"
-)
-
+device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"Using {device} device")
 
 pipe = pipeline(
     "automatic-speech-recognition",
-    model="openai/whisper-large-v3-turbo",
+    model="openai/whisper-large-v3",
     torch_dtype=torch.float16,
     device=device,
 )
 vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")
 
-# --------------------- Settings -------------------- #
-
+# Settings
 target_sample_rate = 24000
 n_mel_channels = 100
 hop_length = 256
 target_rms = 0.1
-nfe_step = 32  # 16, 32
+nfe_step = 32
 cfg_strength = 2.0
 ode_method = "euler"
 sway_sampling_coef = -1.0
 speed = 1.0
-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
+def load_model():
+    model_cfg = dict(dim=1024, depth=24, heads=16, ff_mult=4)
+    ckpt_path = str(cached_path("hf://SWivid/E2-TTS/E2TTS_Base/model_1200000.safetensors"))
     vocab_char_map, vocab_size = get_tokenizer("Emilia_ZH_EN", "pinyin")
     model = CFM(
-        transformer=model_cls(
+        transformer=UNetT(
             **model_cfg, text_num_embeds=vocab_size, mel_dim=n_mel_channels
         ),
         mel_spec_kwargs=dict(
@@ -334,184 +57,17 @@ def load_model(repo_name, exp_name, model_cls, model_cfg, ckpt_step):
         ),
         vocab_char_map=vocab_char_map,
     ).to(device)
-
-    model = load_checkpoint(model, ckpt_path, device, use_ema = True)
-
+    model = load_checkpoint(model, ckpt_path, device, use_ema=True)
     return model
 
+model = load_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 chunk_text(text, max_chars=135):
-    """
-    Splits the input text into chunks, each with a maximum number of characters.
-
-    Args:
-        text (str): The text to be split.
-        max_chars (int): The maximum number of characters per chunk.
-
-    Returns:
-        List[str]: A list of text chunks.
-    """
-    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 len(current_chunk.encode('utf-8')) + len(sentence.encode('utf-8')) <= max_chars:
-            current_chunk += sentence + " " if sentence and len(sentence[-1].encode('utf-8')) == 1 else sentence
-        else:
-            if current_chunk:
-                chunks.append(current_chunk.strip())
-            current_chunk = sentence + " " if sentence and len(sentence[-1].encode('utf-8')) == 1 else sentence
-
-    if current_chunk:
-        chunks.append(current_chunk.strip())
-
-    return chunks
+# Inferencing Logic
 
-@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 == "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 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:
-            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
-
-@gpu_decorator
-def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, cross_fade_duration=0.15):
-
-    print(gen_text)
-
-    gr.Info("Converting audio...")
+def infer(ref_audio, ref_text, gen_text, remove_silence, progress=gr.Progress()):
+    progress(0, desc="Processing audio...")
     with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
-        aseg = AudioSegment.from_file(ref_audio_orig)
-
+        aseg = AudioSegment.from_file(ref_audio)
         non_silent_segs = silence.split_on_silence(
             aseg, min_silence_len=1000, silence_thresh=-50, keep_silence=1000
         )
@@ -519,7 +75,6 @@ def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, cross_fa
         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.")
@@ -527,8 +82,8 @@ def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, cross_fa
         aseg.export(f.name, format="wav")
         ref_audio = f.name
 
+    progress(20, desc="Transcribing audio...")
     if not ref_text.strip():
-        gr.Info("No reference text provided, transcribing reference audio...")
         ref_text = pipe(
             ref_audio,
             chunk_length_s=30,
@@ -536,155 +91,262 @@ def infer(ref_audio_orig, ref_text, gen_text, exp_name, remove_silence, cross_fa
             generate_kwargs={"task": "transcribe"},
             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 += ". "
+        ref_text += ". " if not ref_text.endswith(".") else " "
 
+    progress(40, desc="Generating audio...")
     audio, sr = torchaudio.load(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)
 
-    # Use the new chunk_text function to split gen_text
-    max_chars = int(len(ref_text.encode('utf-8')) / (audio.shape[-1] / sr) * (25 - audio.shape[-1] / sr))
-    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)
+    text_list = [ref_text + gen_text]
+    duration = audio.shape[-1] // hop_length + int(audio.shape[-1] / hop_length / len(ref_text) * len(gen_text) / speed)
+
+    progress(60, desc="Synthesizing speech...")
+    with torch.inference_mode():
+        generated, _ = model.sample(
+            cond=audio,
+            text=text_list,
+            duration=duration,
+            steps=nfe_step,
+            cfg_strength=cfg_strength,
+            sway_sampling_coef=sway_sampling_coef,
+        )
 
+    generated = generated.to(torch.float32)
+    generated = generated[:, audio.shape[-1] // hop_length:, :]
+    generated_mel_spec = generated.permute(0, 2, 1)
+    generated_wave = vocos.decode(generated_mel_spec.cpu())
+    if rms < target_rms:
+        generated_wave = generated_wave * rms / target_rms
 
-@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
+    generated_wave = generated_wave.squeeze().cpu().numpy()
 
-def parse_speechtypes_text(gen_text):
-    # Pattern to find (Emotion)
-    pattern = r'\((.*?)\)'
+    progress(80, desc="Post-processing...")
+    if remove_silence:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+            sf.write(f.name, generated_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")
+            generated_wave, _ = torchaudio.load(f.name)
+        generated_wave = generated_wave.squeeze().cpu().numpy()
 
-    # Split the text by the pattern
-    tokens = re.split(pattern, gen_text)
+    progress(90, desc="Generating spectrogram...")
+    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)
+
+    progress(100, desc="Done!")
+    return (target_sample_rate, generated_wave), spectrogram_path
+
+custom_css = """
+
+/* Dark theme customization */
+:root {
+    --background-fill-primary: #1a1a1a !important;
+    --background-fill-secondary: #2d2d2d !important;
+    --border-color-primary: #404040 !important;
+    --text-color: #ffffff !important;
+    --body-text-color: #ffffff !important;
+    --color-accent-soft: #3d4c7d !important;
+}
+
+body {
+    background-color: #1a1a1a !important;
+    color: #ffffff !important;
+}
+
+.gradio-container {
+    background-color: #1a1a1a !important;
+}
+
+.tabs {
+    background-color: #2d2d2d !important;
+}
+
+.tab-selected {
+    background-color: #404040 !important;
+}
+
+#logo-column {
+    display: flex;
+    justify-content: flex-end;
+    align-items: flex-start;
+    background-color: transparent !important;
+}
+
+#logo-column img {
+    max-width: 180px;
+    height: auto;
+    margin-top: 10px;
+    filter: brightness(0.9);
+}
+
+.gr-box {
+    background-color: #2d2d2d !important;
+    border: 1px solid #404040 !important;
+}
+
+.gr-button {
+    background-color: #3d4c7d !important;
+    color: white !important;
+}
+
+.gr-button:hover {
+    background-color: #4a5d99 !important;
+}
+
+/* Modified input styling for darker background */
+.gr-input, .gr-textarea {
+    background-color: #1a1a1a !important;
+    color: white !important;
+    border: 1px solid #404040 !important;
+}
+
+#step-2-input textarea {
+    background-color: #ffffff !important;
+    color: #000000 !important;
+    border-color: #404040 !important;
+}
+
+#step-2-input textarea:focus {
+    border-color: #3d4c7d !important;
+    box-shadow: 0 0 0 2px rgba(61, 76, 125, 0.2) !important;
+}
+
+#reference-text-input textarea {
+    background-color: #fffff !important;
+    color: #000000!important;
+    border-color: #404040 !important;
+}
+
+#reference-text-input textarea:focus {
+    border-color: #3d4c7d !important;
+    box-shadow: 0 0 0 2px rgba(61, 76, 125, 0.2) !important;
+}
+
+.gr-accordion {
+    background-color: #2d2d2d !important;
+}
+
+.gr-form {
+    background-color: transparent !important;
+}
+
+.markdown-text {
+    color: #ffffff !important;
+}
+
+.markdown-text h1, .markdown-text h2, .markdown-text h3 {
+    color: #ffffff !important;
+}
+
+.audio-player {
+    background-color: #2d2d2d !important;
+    border: 1px solid #404040 !important;
+}
 
-    segments = []
+"""
 
-    current_emotion = 'Regular'
+custom_theme = gr.themes.Soft(
+    primary_hue="indigo",
+    secondary_hue="slate",
+    neutral_hue="slate",
+    font=gr.themes.GoogleFont("Inter"),
+).set(
+    body_background_fill="#1a1a1a",
+    body_background_fill_dark="#1a1a1a",
+    body_text_color="#ffffff",
+    body_text_color_dark="#ffffff",
+    background_fill_primary="#2d2d2d",
+    background_fill_primary_dark="#2d2d2d",
+    background_fill_secondary="#1a1a1a",
+    background_fill_secondary_dark="#1a1a1a",
+    border_color_primary="#404040",
+    border_color_primary_dark="#404040",
+    button_primary_background_fill="#3d4c7d",
+    button_primary_background_fill_dark="#3d4c7d",
+    button_primary_text_color="#ffffff",
+    button_primary_text_color_dark="#ffffff",
+)
 
-    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(theme=custom_theme, css=custom_css) as app:
+    
+    with gr.Row():
+        with gr.Column(scale=9):
+            gr.Markdown(
+                """
+                # Antriksh AI
 
-def update_speed(new_speed):
-    global speed
-    speed = new_speed
-    return f"Speed set to: {speed}"
+                Welcome to our voice cloning application! Follow these steps to create your own custom voice:
 
-with gr.Blocks() as app_credits:
-    gr.Markdown("""
-# Credits
+                1. Upload a short audio clip (less than 15 seconds) of the voice you want to clone.
+                2. Enter the text you want to generate in the new voice.
+                3. Click "Synthesize" and listen to hear the magic!
 
-* [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=["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):
+                It's that easy! Let's get started.
+                """
+            )
+            
+        with gr.Column(scale=1, elem_id="logo-column"):
+            gr.Image("logo/logo-removebg-preview.png", label="", show_label=False)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            ref_audio_input = gr.Audio(
+                label="Step 1: Upload Reference Audio",
+                type="filepath",
+                elem_classes="audio-player"
+            )
+            gen_text_input = gr.Textbox(
+                label="Step 2: Enter Text to Generate",
+                lines=5,
+                elem_id="step-2-input",
+                elem_classes="gr-textarea"
+            )
+            generate_btn = gr.Button(
+                "Step 3: Synthesize",
+                variant="primary",
+                elem_classes="gr-button"
+            )
+
+        with gr.Column(scale=1):
+            audio_output = gr.Audio(
+                label="Generated Audio",
+                elem_classes="audio-player"
+            )
+            spectrogram_output = gr.Image(label="Spectrogram")
+
+    with gr.TabItem("Advanced Settings"):
+        gr.Markdown(
+            "These settings are optional. If you're not sure, leave them as they are."
+        )
         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.",
+            label="Reference Text (Optional)",
+            info="Leave blank for automatic transcription.",
             lines=2,
+            elem_id="reference-text-input",
+            elem_classes="gr-textarea"
         )
         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=speed,
-            step=0.1,
-            info="Adjust the speed of the audio.",
+            info="This can improve the quality of longer audio clips.",
+            value=True
         )
-        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)
-
-    audio_output = gr.Audio(label="Synthesized Audio")
-    spectrogram_output = gr.Image(label="Spectrogram")
 
     generate_btn.click(
         infer,
@@ -692,358 +354,33 @@ with gr.Blocks() as app_tts:
             ref_audio_input,
             ref_text_input,
             gen_text_input,
-            model_choice,
             remove_silence,
-            cross_fade_duration_slider,
         ],
         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")
+    with gr.TabItem("How It Works"):
+        gr.Markdown(
+            """
+            # How Voice Cloning Works
 
-    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)
+            Our voice cloning system uses advanced AI technology to create a synthetic voice that sounds like the reference audio you provide. Here's a simplified explanation of the process:
 
-    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,
-    )
+            1. **Audio Analysis**: When you upload a reference audio clip, our system analyzes its unique characteristics, including pitch, tone, and speech patterns.
 
-def parse_emotional_text(gen_text):
-    # Pattern to find (Emotion)
-    pattern = r'\((.*?)\)'
+            2. **Text Processing**: The text you want to generate is processed and converted into a format that our AI model can understand.
 
-    # Split the text by the pattern
-    tokens = re.split(pattern, gen_text)
+            3. **Voice Synthesis**: Our AI model, based on the E2-TTS (Embarrassingly Easy Text-to-Speech) architecture, combines the characteristics of the reference audio with the new text to generate a synthetic voice.
 
-    segments = []
+            4. **Audio Generation**: The synthetic voice is converted into an audio waveform, which you can then play back or download.
 
-    current_emotion = 'Regular'
+            5. **Spectrogram Creation**: A visual representation of the audio (spectrogram) is generated, showing the frequency content of the sound over time.
 
-    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?!
-    """
-    )
+            This process allows you to generate new speech in the voice of the reference audio, even saying things that weren't in the original recording. It's a powerful tool for creating custom voiceovers, audiobooks, or just for fun!
 
-    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 99 more)
-    max_speech_types = 100
-    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,
+            Remember, the quality of the output depends on the quality and length of the input audio. For best results, use a clear, high-quality audio clip of 10-15 seconds in length.
+            """
         )
 
-    # 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, 0)
-            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(
-        """
-# Antriksh AI
-"""
-    )
-    
-    # Add the image here
-    gr.Image(
-        value="logo\logo-removebg-preview.png",
-        label="AI System Logo",
-        show_label=False,
-        width=300,
-        height=150
-    )
-    
-    gr.TabbedInterface([app_tts, app_podcast, app_emotional, app_credits], ["TTS", "Podcast", "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__":
-    if not USING_SPACES:
-        main()
-    else:
-        app.queue().launch(share=True)
+    app.launch(share=True)