Update app.py

app.py

@@ -1,142 +1,133 @@
 # import whisper
 from faster_whisper import WhisperModel
 import datetime
-# import subprocess
+import subprocess
 import gradio as gr
-# from pathlib import Path
+from pathlib import Path
 import pandas as pd
-# import re
+import re
 import time
 import os
 import numpy as np
 from sklearn.cluster import AgglomerativeClustering
 from sklearn.metrics import silhouette_score
 
-# from pytube import YouTube
-# import yt_dlp
+from pytube import YouTube
+import yt_dlp
 import torch
 import pyannote.audio
 from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
 from pyannote.audio import Audio
 from pyannote.core import Segment
 
-# from gpuinfo import GPUInfo
+from gpuinfo import GPUInfo
 
 import wave
 import contextlib
 from transformers import pipeline
-# import psutil
-
-# import gradio as gr
-import openai
-import os
-# from io import BytesIO
-import tempfile
-from pydub import AudioSegment
-# import shutil
-
+import psutil
 
 whisper_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2"]
 source_languages = {
     "en": "English",
-    # "zh": "Chinese",
-    # "de": "German",
-    # "es": "Spanish",
-    # "ru": "Russian",
-    # "ko": "Korean",
-    # "fr": "French",
+    "zh": "Chinese",
+    "de": "German",
+    "es": "Spanish",
+    "ru": "Russian",
+    "ko": "Korean",
+    "fr": "French",
     "ja": "Japanese",
-    # "pt": "Portuguese",
-    # "tr": "Turkish",
-    # "pl": "Polish",
-    # "ca": "Catalan",
-    # "nl": "Dutch",
-    # "ar": "Arabic",
-    # "sv": "Swedish",
-    # "it": "Italian",
-    # "id": "Indonesian",
-    # "hi": "Hindi",
-    # "fi": "Finnish",
-    # "vi": "Vietnamese",
-    # "he": "Hebrew",
-    # "uk": "Ukrainian",
-    # "el": "Greek",
-    # "ms": "Malay",
-    # "cs": "Czech",
-    # "ro": "Romanian",
-    # "da": "Danish",
-    # "hu": "Hungarian",
-    # "ta": "Tamil",
-    # "no": "Norwegian",
-    # "th": "Thai",
-    # "ur": "Urdu",
-    # "hr": "Croatian",
-    # "bg": "Bulgarian",
-    # "lt": "Lithuanian",
-    # "la": "Latin",
-    # "mi": "Maori",
-    # "ml": "Malayalam",
-    # "cy": "Welsh",
-    # "sk": "Slovak",
-    # "te": "Telugu",
-    # "fa": "Persian",
-    # "lv": "Latvian",
-    # "bn": "Bengali",
-    # "sr": "Serbian",
-    # "az": "Azerbaijani",
-    # "sl": "Slovenian",
-    # "kn": "Kannada",
-    # "et": "Estonian",
-    # "mk": "Macedonian",
-    # "br": "Breton",
-    # "eu": "Basque",
-    # "is": "Icelandic",
-    # "hy": "Armenian",
-    # "ne": "Nepali",
-    # "mn": "Mongolian",
-    # "bs": "Bosnian",
-    # "kk": "Kazakh",
-    # "sq": "Albanian",
-    # "sw": "Swahili",
-    # "gl": "Galician",
-    # "mr": "Marathi",
-    # "pa": "Punjabi",
-    # "si": "Sinhala",
-    # "km": "Khmer",
-    # "sn": "Shona",
-    # "yo": "Yoruba",
-    # "so": "Somali",
-    # "af": "Afrikaans",
-    # "oc": "Occitan",
-    # "ka": "Georgian",
-    # "be": "Belarusian",
-    # "tg": "Tajik",
-    # "sd": "Sindhi",
-    # "gu": "Gujarati",
-    # "am": "Amharic",
-    # "yi": "Yiddish",
-    # "lo": "Lao",
-    # "uz": "Uzbek",
-    # "fo": "Faroese",
-    # "ht": "Haitian creole",
-    # "ps": "Pashto",
-    # "tk": "Turkmen",
-    # "nn": "Nynorsk",
-    # "mt": "Maltese",
-    # "sa": "Sanskrit",
-    # "lb": "Luxembourgish",
-    # "my": "Myanmar",
-    # "bo": "Tibetan",
-    # "tl": "Tagalog",
-    # "mg": "Malagasy",
-    # "as": "Assamese",
-    # "tt": "Tatar",
-    # "haw": "Hawaiian",
-    # "ln": "Lingala",
-    # "ha": "Hausa",
-    # "ba": "Bashkir",
-    # "jw": "Javanese",
-    # "su": "Sundanese",
+    "pt": "Portuguese",
+    "tr": "Turkish",
+    "pl": "Polish",
+    "ca": "Catalan",
+    "nl": "Dutch",
+    "ar": "Arabic",
+    "sv": "Swedish",
+    "it": "Italian",
+    "id": "Indonesian",
+    "hi": "Hindi",
+    "fi": "Finnish",
+    "vi": "Vietnamese",
+    "he": "Hebrew",
+    "uk": "Ukrainian",
+    "el": "Greek",
+    "ms": "Malay",
+    "cs": "Czech",
+    "ro": "Romanian",
+    "da": "Danish",
+    "hu": "Hungarian",
+    "ta": "Tamil",
+    "no": "Norwegian",
+    "th": "Thai",
+    "ur": "Urdu",
+    "hr": "Croatian",
+    "bg": "Bulgarian",
+    "lt": "Lithuanian",
+    "la": "Latin",
+    "mi": "Maori",
+    "ml": "Malayalam",
+    "cy": "Welsh",
+    "sk": "Slovak",
+    "te": "Telugu",
+    "fa": "Persian",
+    "lv": "Latvian",
+    "bn": "Bengali",
+    "sr": "Serbian",
+    "az": "Azerbaijani",
+    "sl": "Slovenian",
+    "kn": "Kannada",
+    "et": "Estonian",
+    "mk": "Macedonian",
+    "br": "Breton",
+    "eu": "Basque",
+    "is": "Icelandic",
+    "hy": "Armenian",
+    "ne": "Nepali",
+    "mn": "Mongolian",
+    "bs": "Bosnian",
+    "kk": "Kazakh",
+    "sq": "Albanian",
+    "sw": "Swahili",
+    "gl": "Galician",
+    "mr": "Marathi",
+    "pa": "Punjabi",
+    "si": "Sinhala",
+    "km": "Khmer",
+    "sn": "Shona",
+    "yo": "Yoruba",
+    "so": "Somali",
+    "af": "Afrikaans",
+    "oc": "Occitan",
+    "ka": "Georgian",
+    "be": "Belarusian",
+    "tg": "Tajik",
+    "sd": "Sindhi",
+    "gu": "Gujarati",
+    "am": "Amharic",
+    "yi": "Yiddish",
+    "lo": "Lao",
+    "uz": "Uzbek",
+    "fo": "Faroese",
+    "ht": "Haitian creole",
+    "ps": "Pashto",
+    "tk": "Turkmen",
+    "nn": "Nynorsk",
+    "mt": "Maltese",
+    "sa": "Sanskrit",
+    "lb": "Luxembourgish",
+    "my": "Myanmar",
+    "bo": "Tibetan",
+    "tl": "Tagalog",
+    "mg": "Malagasy",
+    "as": "Assamese",
+    "tt": "Tatar",
+    "haw": "Hawaiian",
+    "ln": "Lingala",
+    "ha": "Hausa",
+    "ba": "Bashkir",
+    "jw": "Javanese",
+    "su": "Sundanese",
 }
 
 source_language_list = [key[0] for key in source_languages.items()]
@@ -145,7 +136,6 @@ MODEL_NAME = "vumichien/whisper-medium-jp"
 lang = "ja"
 
 device = 0 if torch.cuda.is_available() else "cpu"
-# device = "cpu"
 pipe = pipeline(
     task="automatic-speech-recognition",
     model=MODEL_NAME,
@@ -157,80 +147,79 @@ pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(lan
 
 embedding_model = PretrainedSpeakerEmbedding(
     "speechbrain/spkrec-ecapa-voxceleb",
-    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
+    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
+
+
+def transcribe(microphone, file_upload):
+    warn_output = ""
+    if (microphone is not None) and (file_upload is not None):
+        warn_output = (
+            "WARNING: You've uploaded an audio file and used the microphone. "
+            "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
+        )
+
+    elif (microphone is None) and (file_upload is None):
+        return "ERROR: You have to either use the microphone or upload an audio file"
+
+    file = microphone if microphone is not None else file_upload
+
+    text = pipe(file)["text"]
+
+    return warn_output + text
+
+
+def _return_yt_html_embed(yt_url):
+    video_id = yt_url.split("?v=")[-1]
+    HTML_str = (
+        f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
+        " </center>"
     )
+    return HTML_str
+
 
+def yt_transcribe(yt_url):
+    # yt = YouTube(yt_url)
+    # html_embed_str = _return_yt_html_embed(yt_url)
+    # stream = yt.streams.filter(only_audio=True)[0]
+    # stream.download(filename="audio.mp3")
 
-# def transcribe(microphone, file_upload):
-#     warn_output = ""
-#     if (microphone is not None) and (file_upload is not None):
-#         warn_output = (
-#             "WARNING: You've uploaded an audio file and used the microphone. "
-#             "The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
-#         )
-#
-#     elif (microphone is None) and (file_upload is None):
-#         return "ERROR: You have to either use the microphone or upload an audio file"
-#
-#     file = microphone if microphone is not None else file_upload
-#
-#     text = pipe(file)["text"]
-#
-#     return warn_output + text
-
-
-# def _return_yt_html_embed(yt_url):
-#     video_id = yt_url.split("?v=")[-1]
-#     HTML_str = (
-#         f'<center> <iframe width="500" height="320" src="https://www.youtube.com/embed/{video_id}"> </iframe>'
-#         " </center>"
-#     )
-#     return HTML_str
-
-
-# def yt_transcribe(yt_url):
-#     # yt = YouTube(yt_url)
-#     # html_embed_str = _return_yt_html_embed(yt_url)
-#     # stream = yt.streams.filter(only_audio=True)[0]
-#     # stream.download(filename="audio.mp3")
-#
-#     ydl_opts = {
-#         'format': 'bestvideo*+bestaudio/best',
-#         'postprocessors': [{
-#             'key': 'FFmpegExtractAudio',
-#             'preferredcodec': 'mp3',
-#             'preferredquality': '192',
-#         }],
-#         'outtmpl': 'audio.%(ext)s',
-#     }
-#
-#     with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-#         ydl.download([yt_url])
-#
-#     text = pipe("audio.mp3")["text"]
-#     return html_embed_str, text
+    ydl_opts = {
+        'format': 'bestvideo*+bestaudio/best',
+        'postprocessors': [{
+            'key': 'FFmpegExtractAudio',
+            'preferredcodec': 'mp3',
+            'preferredquality': '192',
+        }],
+        'outtmpl': 'audio.%(ext)s',
+    }
+
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        ydl.download([yt_url])
+
+    text = pipe("audio.mp3")["text"]
+    return html_embed_str, text
 
 
 def convert_time(secs):
     return datetime.timedelta(seconds=round(secs))
 
 
-# def get_youtube(video_url):
-#     # yt = YouTube(video_url)
-#     # abs_video_path = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first().download()
-#
-#     ydl_opts = {
-#         'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best',
-#     }
-#
-#     with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-#         info = ydl.extract_info(video_url, download=False)
-#         abs_video_path = ydl.prepare_filename(info)
-#         ydl.process_info(info)
-#
-#     print("Success download video")
-#     print(abs_video_path)
-#     return abs_video_path
+def get_youtube(video_url):
+    # yt = YouTube(video_url)
+    # abs_video_path = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first().download()
+
+    ydl_opts = {
+        'format': 'bestvideo[ext=mp4]+bestaudio[ext=m4a]/best[ext=mp4]/best',
+    }
+
+    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
+        info = ydl.extract_info(video_url, download=False)
+        abs_video_path = ydl.prepare_filename(info)
+        ydl.process_info(info)
+
+    print("Success download video")
+    print(abs_video_path)
+    return abs_video_path
 
 
 def speech_to_text(video_file_path, selected_source_lang, whisper_model, num_speakers):
@@ -344,110 +333,148 @@ def speech_to_text(video_file_path, selected_source_lang, whisper_model, num_spe
 
         time_end = time.time()
         time_diff = time_end - time_start
-        # memory = psutil.virtual_memory()
-        # gpu_utilization, gpu_memory = GPUInfo.gpu_usage()
-        # gpu_utilization = gpu_utilization[0] if len(gpu_utilization) > 0 else 0
-        # gpu_memory = gpu_memory[0] if len(gpu_memory) > 0 else 0
-        # system_info = f"""
-        # *Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB.*
-        # *Processing time: {time_diff:.5} seconds.*
-        # *GPU Utilization: {gpu_utilization}%, GPU Memory: {gpu_memory}MiB.*
-        # """
+        memory = psutil.virtual_memory()
+        gpu_utilization, gpu_memory = GPUInfo.gpu_usage()
+        gpu_utilization = gpu_utilization[0] if len(gpu_utilization) > 0 else 0
+        gpu_memory = gpu_memory[0] if len(gpu_memory) > 0 else 0
+        system_info = f"""
+        *Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB.* 
+        *Processing time: {time_diff:.5} seconds.*
+        *GPU Utilization: {gpu_utilization}%, GPU Memory: {gpu_memory}MiB.*
+        """
         save_path = "output/transcript_result.csv"
         df_results = pd.DataFrame(objects)
         df_results.to_csv(save_path)
-        # return df_results, system_info, save_path
-        return df_results, save_path
+        return df_results, system_info, save_path
 
     except Exception as e:
         raise RuntimeError("Error Running inference with local model", e)
 
 
-def create_meeting_summary(openai_key, prompt, uploaded_audio, max_transcribe_seconds):
-    openai.api_key = openai_key
-
-    # 音声ファイルを開く
-    audio = AudioSegment.from_file(uploaded_audio)
-
-    # 文字起こしする音声データの上限を設定する
-    if len(audio) > int(max_transcribe_seconds) * 1000:
-        audio = audio[:int(max_transcribe_seconds) * 1000]
-
-    # ファイルサイズを削減するために音声ファイルを圧縮する
-    compressed_audio = audio.set_frame_rate(16000).set_channels(1)
-
-    # 圧縮した音声ファイルをmp3形式で一時ファイルに保存する
-    with tempfile.NamedTemporaryFile(delete=True, suffix=".mp3") as tmp:
-        compressed_audio.export(tmp.name, format="mp3")
-
-        transcript = openai.Audio.transcribe("whisper-1", open(tmp.name, "rb"), response_format="verbose_json")
-        transcript_text = ""
-        for segment in transcript.segments:
-            transcript_text += f"{segment['text']}\n"
-
-    system_template = prompt
-
-    completion = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": system_template},
-            {"role": "user", "content": transcript_text}
-        ]
-    )
-    summary = completion.choices[0].message.content
-    return summary, transcript_text
-
-
 # ---- Gradio Layout -----
+# Inspiration from https://huggingface.co/spaces/RASMUS/Whisper-youtube-crosslingual-subtitles
 video_in = gr.Video(label="Video file", mirror_webcam=False)
-# youtube_url_in = gr.Textbox(label="Youtube url", lines=1, interactive=True)
+youtube_url_in = gr.Textbox(label="Youtube url", lines=1, interactive=True)
 df_init = pd.DataFrame(columns=['Start', 'End', 'Speaker', 'Text'])
-# memory = psutil.virtual_memory()
-selected_source_lang = gr.Dropdown(choices=source_language_list, type="value", value="ja", label="Spoken language in video", interactive=True)
-selected_whisper_model = gr.Dropdown(choices=whisper_models, type="value", value="base", label="Selected Whisper model", interactive=True)
-number_speakers = gr.Number(precision=0, value=0, label="Input number of speakers for better results. If value=0, model will automatic find the best number of speakers", interactive=True)
-# system_info = gr.Markdown(f"*Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB*")
+memory = psutil.virtual_memory()
+selected_source_lang = gr.Dropdown(choices=source_language_list, type="value", value="en",
+                                   label="Spoken language in video", interactive=True)
+selected_whisper_model = gr.Dropdown(choices=whisper_models, type="value", value="base", label="Selected Whisper model",
+                                     interactive=True)
+number_speakers = gr.Number(precision=0, value=0,
+                            label="Input number of speakers for better results. If value=0, model will automatic find the best number of speakers",
+                            interactive=True)
+system_info = gr.Markdown(
+    f"*Memory: {memory.total / (1024 * 1024 * 1024):.2f}GB, used: {memory.percent}%, available: {memory.available / (1024 * 1024 * 1024):.2f}GB*")
 download_transcript = gr.File(label="Download transcript")
-transcription_df = gr.DataFrame(value=df_init,label="Transcription dataframe", row_count=(0, "dynamic"), max_rows = 10, wrap=True, overflow_row_behaviour='paginate')
-# title = "Whisper speaker diarization"
-# demo = gr.Blocks(title=title)
-# demo.encrypt = False
-
-
-inputs = [
-    gr.Textbox(lines=1, label="openai_key", type="password"),
-    gr.TextArea(label="summary prompt", value="""会議の文字起こしが渡されます。
-
-    この会議のサマリーをMarkdown形式で作成してください。サマリーは、以下のような形式で書いてください。
-    - 会議の目的
-    - 会議の内容
-    - 会議の結果
-    """),
-    # gr.Audio(type="filepath", label="音声ファイルをアップロード"),
-    video_in.render(),
-    gr.Textbox(lines=1, label="maximum transcription time (seconds)", type="text"),
-    selected_source_lang.render(),
-    selected_whisper_model.render(),
-    number_speakers.render(),
-]
-
-outputs = [
-    gr.Textbox(label="会議サマリー"),
-    gr.Textbox(label="文字起こし")
-]
-
-app = gr.Interface(
-    fn=create_meeting_summary,
-    inputs=inputs,
-    outputs=outputs,
-    title="会議サマリー生成アプリ",
-    description="音声ファイルをアップロードして、会議のサマリーをMarkdown形式で作成します。"
-)
-
-transcribe_btn = gr.Button("Transcribe audio and diarization")
-transcribe_btn.click(speech_to_text,
-                     [video_in, selected_source_lang, selected_whisper_model, number_speakers],
-                     [transcription_df, download_transcript]
-                     )
-
-app.launch(debug=True)
+transcription_df = gr.DataFrame(value=df_init, label="Transcription dataframe", row_count=(0, "dynamic"), max_rows=10,
+                                wrap=True, overflow_row_behaviour='paginate')
+title = "Whisper speaker diarization"
+demo = gr.Blocks(title=title)
+demo.encrypt = False
+
+with demo:
+    with gr.Tab("Whisper speaker diarization"):
+        gr.Markdown('''
+            <div>
+            <h1 style='text-align: center'>Whisper speaker diarization</h1>
+            This space uses Whisper models from <a href='https://github.com/openai/whisper' target='_blank'><b>OpenAI</b></a> with <a href='https://github.com/guillaumekln/faster-whisper' target='_blank'><b>CTranslate2</b></a> which is a fast inference engine for Transformer models to recognize the speech (4 times faster than original openai model with same accuracy)
+            and ECAPA-TDNN model from <a href='https://github.com/speechbrain/speechbrain' target='_blank'><b>SpeechBrain</b></a> to encode and clasify speakers
+            </div>
+        ''')
+
+        with gr.Row():
+            gr.Markdown('''
+            ### Transcribe youtube link using OpenAI Whisper
+            ##### 1. Using Open AI's Whisper model to seperate audio into segments and generate transcripts.
+            ##### 2. Generating speaker embeddings for each segments.
+            ##### 3. Applying agglomerative clustering on the embeddings to identify the speaker for each segment.
+            ''')
+
+        with gr.Row():
+            gr.Markdown('''
+                ### You can test by following examples:
+                ''')
+        examples = gr.Examples(examples=
+                               ["https://www.youtube.com/watch?v=j7BfEzAFuYc&t=32s",
+                                "https://www.youtube.com/watch?v=-UX0X45sYe4",
+                                "https://www.youtube.com/watch?v=7minSgqi-Gw"],
+                               label="Examples", inputs=[youtube_url_in])
+
+        with gr.Row():
+            with gr.Column():
+                youtube_url_in.render()
+                download_youtube_btn = gr.Button("Download Youtube video")
+                download_youtube_btn.click(get_youtube, [youtube_url_in], [
+                    video_in])
+                print(video_in)
+
+        with gr.Row():
+            with gr.Column():
+                video_in.render()
+                with gr.Column():
+                    gr.Markdown('''
+                    ##### Here you can start the transcription process.
+                    ##### Please select the source language for transcription.
+                    ##### You can select a range of assumed numbers of speakers.
+                    ''')
+                selected_source_lang.render()
+                selected_whisper_model.render()
+                number_speakers.render()
+                transcribe_btn = gr.Button("Transcribe audio and diarization")
+                transcribe_btn.click(speech_to_text,
+                                     [video_in, selected_source_lang, selected_whisper_model, number_speakers],
+                                     [transcription_df, system_info, download_transcript]
+                                     )
+
+        with gr.Row():
+            gr.Markdown('''
+            ##### Here you will get transcription  output
+            ##### ''')
+
+        with gr.Row():
+            with gr.Column():
+                download_transcript.render()
+                transcription_df.render()
+                system_info.render()
+                gr.Markdown(
+                    '''<center><img src='https://visitor-badge.glitch.me/badge?page_id=WhisperDiarizationSpeakers' alt='visitor badge'><a href="https://opensource.org/licenses/Apache-2.0"><img src='https://img.shields.io/badge/License-Apache_2.0-blue.svg' alt='License: Apache 2.0'></center>''')
+
+    with gr.Tab("Whisper Transcribe Japanese Audio"):
+        gr.Markdown(f'''
+              <div>
+              <h1 style='text-align: center'>Whisper Transcribe Japanese Audio</h1>
+              </div>
+              Transcribe long-form microphone or audio inputs with the click of a button! The fine-tuned
+              checkpoint <a href='https://huggingface.co/{MODEL_NAME}' target='_blank'><b>{MODEL_NAME}</b></a> to transcribe audio files of arbitrary length.
+          ''')
+        microphone = gr.inputs.Audio(source="microphone", type="filepath", optional=True)
+        upload = gr.inputs.Audio(source="upload", type="filepath", optional=True)
+        transcribe_btn = gr.Button("Transcribe Audio")
+        text_output = gr.Textbox()
+        with gr.Row():
+            gr.Markdown('''
+                ### You can test by following examples:
+                ''')
+        examples = gr.Examples(examples=
+                               ["sample1.wav",
+                                "sample2.wav",
+                                ],
+                               label="Examples", inputs=[upload])
+        transcribe_btn.click(transcribe, [microphone, upload], outputs=text_output)
+
+    with gr.Tab("Whisper Transcribe Japanese YouTube"):
+        gr.Markdown(f'''
+              <div>
+              <h1 style='text-align: center'>Whisper Transcribe Japanese YouTube</h1>
+              </div>
+                Transcribe long-form YouTube videos with the click of a button! The fine-tuned checkpoint:
+                <a href='https://huggingface.co/{MODEL_NAME}' target='_blank'><b>{MODEL_NAME}</b></a> to transcribe audio files of arbitrary length.
+            ''')
+        youtube_link = gr.Textbox(label="Youtube url", lines=1, interactive=True)
+        yt_transcribe_btn = gr.Button("Transcribe YouTube")
+        text_output2 = gr.Textbox()
+        html_output = gr.Markdown()
+        yt_transcribe_btn.click(yt_transcribe, [youtube_link], outputs=[html_output, text_output2])
+
+demo.launch(debug=True)