Update app.py

app.py

@@ -1,54 +1,36 @@
-import sys
-import io, os, stat
-import subprocess
-import random
-from zipfile import ZipFile
+import os
 import uuid
 import time
 import torch
+import gradio as gr
 import torchaudio
-
-
-#download for mecab
-os.system('python -m unidic download')
-
-# By using XTTS you agree to CPML license https://coqui.ai/cpml
-os.environ["COQUI_TOS_AGREED"] = "1"
-
-# langid is used to detect language for longer text
-# Most users expect text to be their own language, there is checkbox to disable it
-import langid
-import base64
-import csv
+import subprocess
+import numpy as np
+from zipfile import ZipFile
 from io import StringIO
+import csv
 import datetime
-import re
-
-import gradio as gr
-from scipy.io.wavfile import write
-from pydub import AudioSegment
-
+import langid
 from TTS.api import TTS
 from TTS.tts.configs.xtts_config import XttsConfig
 from TTS.tts.models.xtts import Xtts
 from TTS.utils.generic_utils import get_user_data_dir
-
-HF_TOKEN = os.environ.get("HF_TOKEN")
-
 from huggingface_hub import HfApi
 
-# will use api to restart space on a unrecoverable error
+# Set up environment and API
+os.environ["COQUI_TOS_AGREED"] = "1"
+HF_TOKEN = os.environ.get("HF_TOKEN")
 api = HfApi(token=HF_TOKEN)
-repo_id = "coqui/xtts"
+repo_id = "your/repo-id"  # Replace with your repository ID
 
-# Use never ffmpeg binary for Ubuntu20 to use denoising for microphone input
+# Download and set up ffmpeg
 print("Export newer ffmpeg binary for denoise filter")
 ZipFile("ffmpeg.zip").extractall()
 print("Make ffmpeg binary executable")
 st = os.stat("ffmpeg")
 os.chmod("ffmpeg", st.st_mode | stat.S_IEXEC)
 
-# This will trigger downloading model
+# Load XTTS model
 print("Downloading if not downloaded Coqui XTTS V2")
 from TTS.utils.manage import ModelManager
 
@@ -66,17 +48,12 @@ model.load_checkpoint(
     checkpoint_path=os.path.join(model_path, "model.pth"),
     vocab_path=os.path.join(model_path, "vocab.json"),
     eval=True,
-    use_deepspeed=True,
+    use_deepspeed=False,  # Adjust based on your setup
 )
-model.cuda()
-
-# This is for debugging purposes only
-DEVICE_ASSERT_DETECTED = 0
-DEVICE_ASSERT_PROMPT = None
-DEVICE_ASSERT_LANG = None
-
-supported_languages = config.languages
+# Ensure model is on CPU
+model.cpu()
 
+# Function for prediction
 def predict(
     prompt,
     language,
@@ -87,617 +64,149 @@ def predict(
     no_lang_auto_detect,
     agree,
 ):
-    if agree == True:
-        if language not in supported_languages:
-            gr.Warning(
-                f"Language you put {language} in is not in is not in our Supported Languages, please choose from dropdown"
-            )
-
-            return (
-                None,
-                None,
-                None,
-                None,
-            )
-
-        language_predicted = langid.classify(prompt)[
-            0
-        ].strip()  # strip need as there is space at end!
-
-        # tts expects chinese as zh-cn
-        if language_predicted == "zh":
-            # we use zh-cn
-            language_predicted = "zh-cn"
-
-        print(f"Detected language:{language_predicted}, Chosen language:{language}")
-
-        # After text character length 15 trigger language detection
-        if len(prompt) > 15:
-            # allow any language for short text as some may be common
-            # If user unchecks language autodetection it will not trigger
-            # You may remove this completely for own use
-            if language_predicted != language and not no_lang_auto_detect:
-                # Please duplicate and remove this check if you really want this
-                # Or auto-detector fails to identify language (which it can on pretty short text or mixed text)
-                gr.Warning(
-                    f"It looks like your text isn’t the language you chose , if you’re sure the text is the same language you chose, please check disable language auto-detection checkbox"
-                )
-
-                return (
-                    None,
-                    None,
-                    None,
-                    None,
-                )
-
-        if use_mic == True:
-            if mic_file_path is not None:
-                speaker_wav = mic_file_path
-            else:
-                gr.Warning(
-                    "Please record your voice with Microphone, or uncheck Use Microphone to use reference audios"
-                )
-                return (
-                    None,
-                    None,
-                    None,
-                    None,
-                )
-
-        else:
-            speaker_wav = audio_file_pth
-
-        # Filtering for microphone input, as it has BG noise, maybe silence in beginning and end
-        # This is fast filtering not perfect
-
-        # Apply all on demand
-        lowpassfilter = denoise = trim = loudness = True
-
-        if lowpassfilter:
-            lowpass_highpass = "lowpass=8000,highpass=75,"
-        else:
-            lowpass_highpass = ""
-
-        if trim:
-            # better to remove silence in beginning and end for microphone
-            trim_silence = "areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,"
-        else:
-            trim_silence = ""
-
-        if voice_cleanup:
-            try:
-                out_filename = (
-                    speaker_wav + str(uuid.uuid4()) + ".wav"
-                )  # ffmpeg to know output format
-
-                # we will use newer ffmpeg as that has afftn denoise filter
-                shell_command = f"./ffmpeg -y -i {speaker_wav} -af {lowpass_highpass}{trim_silence} {out_filename}".split(
-                    " "
-                )
-
-                command_result = subprocess.run(
-                    [item for item in shell_command],
-                    capture_output=False,
-                    text=True,
-                    check=True,
-                )
-                speaker_wav = out_filename
-                print("Filtered microphone input")
-            except subprocess.CalledProcessError:
-                # There was an error - command exited with non-zero code
-                print("Error: failed filtering, use original microphone input")
-        else:
-            speaker_wav = speaker_wav
-
-        if len(prompt) < 2:
-            gr.Warning("Please give a longer prompt text")
-            return (
-                None,
-                None,
-                None,
-                None,
-            )
-        if len(prompt) > 200:
-            gr.Warning(
-                "Text length limited to 200 characters for this demo, please try shorter text. You can clone this space and edit code for your own usage"
-            )
-            return (
-                None,
-                None,
-                None,
-                None,
-            )
-        global DEVICE_ASSERT_DETECTED
-        if DEVICE_ASSERT_DETECTED:
-            global DEVICE_ASSERT_PROMPT
-            global DEVICE_ASSERT_LANG
-            # It will likely never come here as we restart space on first unrecoverable error now
-            print(
-                f"Unrecoverable exception caused by language:{DEVICE_ASSERT_LANG} prompt:{DEVICE_ASSERT_PROMPT}"
-            )
-
-            # HF Space specific.. This error is unrecoverable need to restart space
-            space = api.get_space_runtime(repo_id=repo_id)
-            if space.stage!="BUILDING":
-                api.restart_space(repo_id=repo_id)
-            else:
-                print("TRIED TO RESTART but space is building")
+    if not agree:
+        gr.Warning("Please accept the Terms & Condition!")
+        return (None, None, None, None)
+
+    if language not in config.languages:
+        gr.Warning(f"Language not supported. Please choose from dropdown.")
+        return (None, None, None, None)
+
+    language_predicted = langid.classify(prompt)[0].strip()
+    if language_predicted == "zh":
+        language_predicted = "zh-cn"
+
+    if len(prompt) < 2:
+        gr.Warning("Please provide a longer prompt text.")
+        return (None, None, None, None)
+    if len(prompt) > 200:
+        gr.Warning("Text length limited to 200 characters.")
+        return (None, None, None, None)
+
+    if use_mic:
+        if mic_file_path is None:
+            gr.Warning("Please record your voice with Microphone.")
+            return (None, None, None, None)
+        speaker_wav = mic_file_path
+    else:
+        speaker_wav = audio_file_pth
 
+    if voice_cleanup:
         try:
-            metrics_text = ""
-            t_latent = time.time()
-
-            # note diffusion_conditioning not used on hifigan (default mode), it will be empty but need to pass it to model.inference
-            try:
-                (
-                    gpt_cond_latent,
-                    speaker_embedding,
-                ) = model.get_conditioning_latents(audio_path=speaker_wav, gpt_cond_len=30, gpt_cond_chunk_len=4, max_ref_length=60)
-            except Exception as e:
-                print("Speaker encoding error", str(e))
-                gr.Warning(
-                    "It appears something wrong with reference, did you unmute your microphone?"
-                )
-                return (
-                    None,
-                    None,
-                    None,
-                    None,
-                )
-
-            latent_calculation_time = time.time() - t_latent
-            # metrics_text=f"Embedding calculation time: {latent_calculation_time:.2f} seconds\n"
-
-            # temporary comma fix
-            prompt= re.sub("([^\x00-\x7F]|\w)(\.|\。|\?)",r"\1 \2\2",prompt)
-
-            wav_chunks = []
-            ## Direct mode
-            
-            print("I: Generating new audio...")
-            t0 = time.time()
-            out = model.inference(
-                prompt,
-                language,
-                gpt_cond_latent,
-                speaker_embedding,
-                repetition_penalty=5.0,
-                temperature=0.75,
-            )
-            inference_time = time.time() - t0
-            print(f"I: Time to generate audio: {round(inference_time*1000)} milliseconds")
-            metrics_text+=f"Time to generate audio: {round(inference_time*1000)} milliseconds\n"
-            real_time_factor= (time.time() - t0) / out['wav'].shape[-1] * 24000
-            print(f"Real-time factor (RTF): {real_time_factor}")
-            metrics_text+=f"Real-time factor (RTF): {real_time_factor:.2f}\n"
-            torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
-
-
-            """
-            print("I: Generating new audio in streaming mode...")
-            t0 = time.time()
-            chunks = model.inference_stream(
-                prompt,
-                language,
-                gpt_cond_latent,
-                speaker_embedding,
-                repetition_penalty=7.0,
-                temperature=0.85,
-            )
-
-            first_chunk = True
-            for i, chunk in enumerate(chunks):
-                if first_chunk:
-                    first_chunk_time = time.time() - t0
-                    metrics_text += f"Latency to first audio chunk: {round(first_chunk_time*1000)} milliseconds\n"
-                    first_chunk = False
-                wav_chunks.append(chunk)
-                print(f"Received chunk {i} of audio length {chunk.shape[-1]}")
-            inference_time = time.time() - t0
-            print(
-                f"I: Time to generate audio: {round(inference_time*1000)} milliseconds"
-            )
-            #metrics_text += (
-            #    f"Time to generate audio: {round(inference_time*1000)} milliseconds\n"
-            #)
-
-            wav = torch.cat(wav_chunks, dim=0)
-            print(wav.shape)
-            real_time_factor = (time.time() - t0) / wav.shape[0] * 24000
-            print(f"Real-time factor (RTF): {real_time_factor}")
-            metrics_text += f"Real-time factor (RTF): {real_time_factor:.2f}\n"
-
-            torchaudio.save("output.wav", wav.squeeze().unsqueeze(0).cpu(), 24000)
-            """
-
-        except RuntimeError as e:
-            if "device-side assert" in str(e):
-                # cannot do anything on cuda device side error, need tor estart
-                print(
-                    f"Exit due to: Unrecoverable exception caused by language:{language} prompt:{prompt}",
-                    flush=True,
-                )
-                gr.Warning("Unhandled Exception encounter, please retry in a minute")
-                print("Cuda device-assert Runtime encountered need restart")
-                if not DEVICE_ASSERT_DETECTED:
-                    DEVICE_ASSERT_DETECTED = 1
-                    DEVICE_ASSERT_PROMPT = prompt
-                    DEVICE_ASSERT_LANG = language
-
-                # just before restarting save what caused the issue so we can handle it in future
-                # Uploading Error data only happens for unrecovarable error
-                error_time = datetime.datetime.now().strftime("%d-%m-%Y-%H:%M:%S")
-                error_data = [
-                    error_time,
-                    prompt,
-                    language,
-                    audio_file_pth,
-                    mic_file_path,
-                    use_mic,
-                    voice_cleanup,
-                    no_lang_auto_detect,
-                    agree,
-                ]
-                error_data = [str(e) if type(e) != str else e for e in error_data]
-                print(error_data)
-                print(speaker_wav)
-                write_io = StringIO()
-                csv.writer(write_io).writerows([error_data])
-                csv_upload = write_io.getvalue().encode()
-
-                filename = error_time + "_" + str(uuid.uuid4()) + ".csv"
-                print("Writing error csv")
-                error_api = HfApi()
-                error_api.upload_file(
-                    path_or_fileobj=csv_upload,
-                    path_in_repo=filename,
-                    repo_id="coqui/xtts-flagged-dataset",
-                    repo_type="dataset",
-                )
-
-                # speaker_wav
-                print("Writing error reference audio")
-                speaker_filename = (
-                    error_time + "_reference_" + str(uuid.uuid4()) + ".wav"
-                )
-                error_api = HfApi()
-                error_api.upload_file(
-                    path_or_fileobj=speaker_wav,
-                    path_in_repo=speaker_filename,
-                    repo_id="coqui/xtts-flagged-dataset",
-                    repo_type="dataset",
-                )
-
-                # HF Space specific.. This error is unrecoverable need to restart space
-                space = api.get_space_runtime(repo_id=repo_id)
-                if space.stage!="BUILDING":
-                    api.restart_space(repo_id=repo_id)
-                else:
-                    print("TRIED TO RESTART but space is building")
-                    
-            else:
-                if "Failed to decode" in str(e):
-                    print("Speaker encoding error", str(e))
-                    gr.Warning(
-                        "It appears something wrong with reference, did you unmute your microphone?"
-                    )
-                else:
-                    print("RuntimeError: non device-side assert error:", str(e))
-                    gr.Warning("Something unexpected happened please retry again.")
-                return (
-                    None,
-                    None,
-                    None,
-                    None,
-                )
-        return (
-            gr.make_waveform(
-                audio="output.wav",
-            ),
-            "output.wav",
-            metrics_text,
-            speaker_wav,
-        )
+            out_filename = f"{speaker_wav}_{uuid.uuid4()}.wav"
+            shell_command = f"./ffmpeg -y -i {speaker_wav} -af lowpass=8000,highpass=75,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02,areverse,silenceremove=start_periods=1:start_silence=0:start_threshold=0.02 {out_filename}".split()
+            subprocess.run(shell_command, capture_output=False, text=True, check=True)
+            speaker_wav = out_filename
+        except subprocess.CalledProcessError:
+            print("Error filtering audio.")
     else:
-        gr.Warning("Please accept the Terms & Condition!")
-        return (
-            None,
-            None,
-            None,
-            None,
-        )
-
-
-title = "Coqui🐸 XTTS"
-
-description = """
-
-<br/>
-
-This demo is currently running **XTTS v2.0.3** <a href="https://huggingface.co/coqui/XTTS-v2">XTTS</a> is a multilingual text-to-speech and voice-cloning model. This demo features zero-shot voice cloning, however, you can fine-tune XTTS for better results. Leave a star 🌟 on Github <a href="https://github.com/coqui-ai/TTS">🐸TTS</a>, where our open-source inference and training code lives.
-
-<br/>
-
-Supported languages: Arabic: ar, Brazilian Portuguese: pt , Mandarin Chinese: zh-cn, Czech: cs, Dutch: nl, English: en, French: fr, German: de, Italian: it, Polish: pl, Russian: ru, Spanish: es, Turkish: tr, Japanese: ja, Korean: ko, Hungarian: hu, Hindi: hi
-
-<br/>
-"""
-
-links = """
-<img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=0d00920c-8cc9-4bf3-90f2-a615797e5f59" />
-
-|                                 |                                         |
-| ------------------------------- | --------------------------------------- |
-| 🐸💬 **CoquiTTS**                | <a style="display:inline-block" href='https://github.com/coqui-ai/TTS'><img src='https://img.shields.io/github/stars/coqui-ai/TTS?style=social' /></a>|
-| 💼 **Documentation**            | [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
-| 👩‍💻 **Questions**                | [GitHub Discussions](https://github.com/coqui-ai/TTS/discussions) |
-| 🗯 **Community**         | [![Dicord](https://img.shields.io/discord/1037326658807533628?color=%239B59B6&label=chat%20on%20discord)](https://discord.gg/5eXr5seRrv)  |
-
-
-"""
-
-article = """
-<div style='margin:20px auto;'>
-<p>By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml</p>
-<p>We collect data only for error cases for improvement.</p>
-</div>
-"""
-examples = [
-    [
-        "Once when I was six years old I saw a magnificent picture",
-        "en",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Lorsque j'avais six ans j'ai vu, une fois, une magnifique image",
-        "fr",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Als ich sechs war, sah ich einmal ein wunderbares Bild",
-        "de",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Cuando tenía seis años, vi una vez una imagen magnífica",
-        "es",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Quando eu tinha seis anos eu vi, uma vez, uma imagem magnífica",
-        "pt",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Kiedy miałem sześć lat, zobaczyłem pewnego razu wspaniały obrazek",
-        "pl",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Un tempo lontano, quando avevo sei anni, vidi un magnifico disegno",
-        "it",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Bir zamanlar, altı yaşındayken, muhteşem bir resim gördüm",
-        "tr",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Когда мне было шесть лет, я увидел однажды удивительную картинку",
-        "ru",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Toen ik een jaar of zes was, zag ik op een keer een prachtige plaat",
-        "nl",
-        "examples/male.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "Když mi bylo šest let, viděl jsem jednou nádherný obrázek",
-        "cs",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "当我还只有六岁的时候， 看到了一副精彩的插画",
-        "zh-cn",
-        "examples/female.wav",
-        None,
-        False,
-        False,
-        False,
-        True,
-    ],
-    [
-        "かつて 六歳のとき、素晴らしい絵を見ました",
-        "ja",
-        "examples/female.wav",
-        None,
-        False,
-        True,
-        False,
-        True,
-    ],
-    [
-        "한번은 내가 여섯 살이었을 때 멋진 그림을 보았습니다.",
-        "ko",
-        "examples/female.wav",
-        None,
-        False,
-        True,
-        False,
-        True,
-    ],
-        [
-        "Egyszer hat éves koromban láttam egy csodálatos képet",
-        "hu",
-        "examples/male.wav",
-        None,
-        False,
-        True,
-        False,
-        True,
-    ],
-]
+        speaker_wav = speaker_wav
 
+    try:
+        metrics_text = ""
+        t_latent = time.time()
 
+        gpt_cond_latent, speaker_embedding = model.get_conditioning_latents(
+            audio_path=speaker_wav,
+            gpt_cond_len=30,
+            gpt_cond_chunk_len=4,
+            max_ref_length=60
+        )
 
+        latent_calculation_time = time.time() - t_latent
+        prompt = re.sub("([^\x00-\x7F]|\w)(\.|\。|\?)", r"\1 \2\2", prompt)
+
+        print("Generating audio...")
+        t0 = time.time()
+        out = model.inference(
+            prompt,
+            language,
+            gpt_cond_latent,
+            speaker_embedding,
+            repetition_penalty=5.0,
+            temperature=0.75,
+        )
+        inference_time = time.time() - t0
+        metrics_text += f"Time to generate audio: {round(inference_time * 1000)} milliseconds\n"
+        real_time_factor = (time.time() - t0) / out['wav'].shape[-1] * 24000
+        metrics_text += f"Real-time factor (RTF): {real_time_factor:.2f}\n"
+        torchaudio.save("output.wav", torch.tensor(out["wav"]).unsqueeze(0), 24000)
+
+    except RuntimeError as e:
+        print(f"RuntimeError: {str(e)}")
+        gr.Warning("An error occurred. Please try again.")
+        return (None, None, None, None)
+
+    return (
+        gr.make_waveform(audio="output.wav"),
+        "output.wav",
+        metrics_text,
+        speaker_wav,
+    )
+
+# Gradio interface
 with gr.Blocks(analytics_enabled=False) as demo:
     with gr.Row():
         with gr.Column():
-            gr.Markdown(
-                """
-                ## <img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>
-                """
-            )
+            gr.Markdown("## XTTS Demo")
         with gr.Column():
-            # placeholder to align the image
             pass
 
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown(description)
-        with gr.Column():
-            gr.Markdown(links)
-
     with gr.Row():
         with gr.Column():
             input_text_gr = gr.Textbox(
                 label="Text Prompt",
-                info="One or two sentences at a time is better. Up to 200 text characters.",
-                value="Hi there, I'm your new voice clone. Try your best to upload quality audio.",
+                info="One or two sentences at a time. Up to 200 characters.",
+                value="Hello! Try your best to upload quality audio.",
             )
             language_gr = gr.Dropdown(
                 label="Language",
-                info="Select an output language for the synthesised speech",
                 choices=[
-                    "en",
-                    "es",
-                    "fr",
-                    "de",
-                    "it",
-                    "pt",
-                    "pl",
-                    "tr",
-                    "ru",
-                    "nl",
-                    "cs",
-                    "ar",
-                    "zh-cn",
-                    "ja",
-                    "ko",
-                    "hu",
-                    "hi"
+                    "en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl",
+                    "cs", "ar", "zh-cn", "ja", "ko", "hu", "hi"
                 ],
-                max_choices=1,
                 value="en",
             )
             ref_gr = gr.Audio(
                 label="Reference Audio",
-                info="Click on the ✎ button to upload your own target speaker audio",
                 type="filepath",
                 value="examples/female.wav",
             )
             mic_gr = gr.Audio(
                 source="microphone",
                 type="filepath",
-                info="Use your microphone to record audio",
                 label="Use Microphone for Reference",
             )
             use_mic_gr = gr.Checkbox(
                 label="Use Microphone",
                 value=False,
-                info="Notice: Microphone input may not work properly under traffic",
             )
             clean_ref_gr = gr.Checkbox(
                 label="Cleanup Reference Voice",
                 value=False,
-                info="This check can improve output if your microphone or reference voice is noisy",
             )
             auto_det_lang_gr = gr.Checkbox(
-                label="Do not use language auto-detect",
+                label="Disable Language Auto-Detect",
                 value=False,
-                info="Check to disable language auto-detection",
             )
             tos_gr = gr.Checkbox(
                 label="Agree",
                 value=False,
-                info="I agree to the terms of the CPML: https://coqui.ai/cpml",
             )
 
-            tts_button = gr.Button("Send", elem_id="send-btn", visible=True)
-
+            tts_button = gr.Button("Send")
 
         with gr.Column():
             video_gr = gr.Video(label="Waveform Visual")
-            audio_gr = gr.Audio(label="Synthesised Audio", autoplay=True)
+            audio_gr = gr.Audio(label="Synthesized Audio", autoplay=True)
             out_text_gr = gr.Text(label="Metrics")
             ref_audio_gr = gr.Audio(label="Reference Audio Used")
 
-    with gr.Row():
-        gr.Examples(examples,
-                    label="Examples",
-                    inputs=[input_text_gr, language_gr, ref_gr, mic_gr, use_mic_gr, clean_ref_gr, auto_det_lang_gr, tos_gr],
-                    outputs=[video_gr, audio_gr, out_text_gr, ref_audio_gr],
-                    fn=predict,
-                    cache_examples=False,)
-
-    tts_button.click(predict, [input_text_gr, language_gr, ref_gr, mic_gr, use_mic_gr, clean_ref_gr, auto_det_lang_gr, tos_gr], outputs=[video_gr, audio_gr, out_text_gr, ref_audio_gr])
+    tts_button.click(
+        predict,
+        inputs=[input_text_gr, language_gr, ref_gr, mic_gr, use_mic_gr, clean_ref_gr, auto_det_lang_gr, tos_gr],
+        outputs=[video_gr, audio_gr, out_text_gr, ref_audio_gr]
+    )
 
-demo.queue()  
-demo.launch(debug=True, show_api=True)
+demo.queue()
+demo.launch(debug=True)