Update app.py

app.py

@@ -1,443 +1,288 @@
-# import whisper
-from faster_whisper import WhisperModel
-import datetime
-import subprocess
-import gradio as gr
-from pathlib import Path
-import pandas as pd
-import re
-import time
 import os
+import wave
 import numpy as np
-from sklearn.cluster import AgglomerativeClustering
-from sklearn.metrics import silhouette_score
-
-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
+import contextlib
+from pydub import AudioSegment
 from pyannote.core import Segment
+from pyannote.audio import Audio
+from pyannote.audio.pipelines.speaker_verification import PretrainedSpeakerEmbedding
+import torch
+from typing import Dict, List, Tuple
 
-from gpuinfo import GPUInfo
 
-import wave
-import contextlib
-from transformers import pipeline
-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",
-    "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",
-}
-
-source_language_list = [key[0] for key in source_languages.items()]
-
-MODEL_NAME = "vumichien/whisper-medium-jp"
-lang = "ja"
-
-device = 0 if torch.cuda.is_available() else "cpu"
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device=device,
-)
-os.makedirs('output', exist_ok=True)
-pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
+def convert_to_wav(input_file: str, output_file: str = "output_file.wav") -> str:
+    """
+    音声ファイルをWAV形式に変換します。
+
+    Parameters
+    ----------
+    input_file: str
+        変換する音声ファイルのパス
+    output_file: str
+        変換後のWAVファイルの出力先パス（デフォルトは"output_file.wav"）
+    Returns
+    -------
+    str
+        変換後のWAVファイルのパス
+    """
+    file_format = os.path.splitext(input_file)[1][1:]
+    audio = AudioSegment.from_file(input_file, format=file_format)
+    audio.export(output_file, format="wav")
+    return output_file
+
+
+def segment_embedding(
+        file_name: str,
+        duration: float,
+        segment,
+        embedding_model: PretrainedSpeakerEmbedding
+) -> np.ndarray:
+    """
+    音声ファイルから指定されたセグメントの埋め込みを計算します。
+
+    Parameters
+    ----------
+    file_name: str
+        音声ファイルのパス
+    duration: float
+        音声ファイルの継続時間
+    segment: whisperのtranscribeのsegment
+    embedding_model: PretrainedSpeakerEmbedding
+        埋め込みモデル
+    Returns
+    -------
+    np.ndarray
+        計算された埋め込みベクトル
+    """
+    audio = Audio()
+    start = segment["start"]
+    end = min(duration, segment["end"])
+    clip = Segment(start, end)
+    waveform, sample_rate = audio.crop(file_name, clip)
+    return embedding_model(waveform[None])
 
-embedding_model = PretrainedSpeakerEmbedding(
-    "speechbrain/spkrec-ecapa-voxceleb",
-    device=torch.device("cuda" if torch.cuda.is_available() else "cpu"))
 
+def reference_audio_embedding(
+        file_name: str
+) -> np.ndarray:
+    """
+    参考音声の埋め込みを出力します。
+
+    Parameters
+    ----------
+    file_name: str
+        音声ファイルのパス
+    Returns
+    -------
+    np.ndarray
+        計算された埋め込みベクトル
+    """
+    audio = Audio()
+    waveform, sample_rate = audio(file_name)
+    embedding_model = embedding_model = PretrainedSpeakerEmbedding("speechbrain/spkrec-ecapa-voxceleb", device='cpu')
+    return embedding_model(waveform[None])[0]
 
-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"
+def generate_speaker_embeddings(
+        meeting_file_path: str,
+        transcript
+) -> np.ndarray:
+    """
+    音声ファイルから話者の埋め込みを計算します。
+
+    Parameters
+    ----------
+    meeting_file_path: str
+        音声ファイルのパス
+    transcript: Whisper API の transcribe メソッドの出力結果
+    Returns
+    -------
+    np.ndarray
+        計算された話者の埋め込み群
+    """
+    output_file = convert_to_wav(meeting_file_path)
 
-    file = microphone if microphone is not None else file_upload
+    segments = transcript['segments']
+    embedding_model = PretrainedSpeakerEmbedding("speechbrain/spkrec-ecapa-voxceleb", device='cpu')
+    embeddings = np.zeros(shape=(len(segments), 192))
 
-    text = pipe(file)["text"]
+    with contextlib.closing(wave.open(output_file, 'r')) as f:
+        frames = f.getnframes()
+        rate = f.getframerate()
+        duration = frames / float(rate)
 
-    return warn_output + text
+    for i, segment in enumerate(segments):
+        embeddings[i] = segment_embedding(output_file, duration, segment, embedding_model)
 
+    embeddings = np.nan_to_num(embeddings)
+    return embeddings
 
-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
 
+import numpy as np
+from sklearn.cluster import AgglomerativeClustering
+from typing import List, Tuple
 
-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',
-    }
+def clustering_embeddings(speaker_count: int, embeddings: np.ndarray) -> AgglomerativeClustering:
+    """
+    埋め込みデータをクラスタリングして、クラスタリングオブジェクトを返します。
+    Parameters
+    ----------
+    embeddings: np.ndarray
+        分散表現（埋め込み）のリスト。
+    Returns
+    -------
+    AgglomerativeClustering
+        クラスタリングオブジェクト。
+    """
+    clustering = AgglomerativeClustering(speaker_count).fit(embeddings)
+    return clustering
 
-    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
-        ydl.download([yt_url])
 
-    text = pipe("audio.mp3")["text"]
-    return html_embed_str, text
+def format_speaker_output_by_segment(clustering: AgglomerativeClustering, transcript: dict) -> str:
+    """
+    クラスタリングの結果をもとに、各発話者ごとにセグメントを整形して出力します
+    Parameters
+    ----------
+    clustering: AgglomerativeClustering
+        クラスタリングオブジェクト。
+    transcript: dict
+        Whisper API の transcribe メソッドの出力結果
+    Returns
+    -------
+    str
+        発話者ごとに整形されたセグメントの文字列
+    """
+    labeled_segments = []
+    for label, segment in zip(clustering.labels_, transcript["segments"]):
+        labeled_segments.append((label, segment["start"], segment["text"]))
 
+    output = ""
+    for speaker, _, text in labeled_segments:
+        output += f"話者{speaker + 1}: 「{text}」\n"
+    return output
 
-def convert_time(secs):
-    return datetime.timedelta(seconds=round(secs))
 
+from sklearn.cluster import KMeans
+from sklearn.metrics.pairwise import pairwise_distances
 
-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',
-    }
+def clustering_embeddings2(speaker_count: int, embeddings: np.ndarray) -> KMeans:
+    """
+    埋め込みデータをクラスタリングして、クラスタリングオブジェクトを返します。
+    Parameters
+    ----------
+    embeddings: np.ndarray
+        分散表現（埋め込み）のリスト。
+    Returns
+    -------
+    KMeans
+        クラスタリングオブジェクト。
+    """
+    # コサイン類似度行列を計算
+    cosine_distances = pairwise_distances(embeddings, metric='cosine')
+    clustering = KMeans(n_clusters=speaker_count).fit(cosine_distances)
+    return clustering
 
-    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
+from scipy.spatial.distance import cosine
 
 
-def speech_to_text(video_file_path, selected_source_lang, whisper_model, num_speakers):
+def closest_reference_speaker(embedding: np.ndarray, references: List[Tuple[str, np.ndarray]]) -> str:
     """
-    # 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.
+    与えられた埋め込みに最も近い参照話者を返します。
+    Parameters
+    ----------
+    embedding: np.ndarray
+        話者の埋め込み
+    references: List[Tuple[str, np.ndarray]]
+        参照話者の名前と埋め込みのリスト
+    Returns
+    -------
+    str
+        最も近い参照話者の名前
+    """
+    min_distance = float('inf')
+    closest_speaker = None
+    for name, reference_embedding in references:
+        distance = cosine(embedding, reference_embedding)
+        if distance < min_distance:
+            min_distance = distance
+            closest_speaker = name
+
+    return closest_speaker
+
 
-    Speech Recognition is based on models from OpenAI Whisper https://github.com/openai/whisper
-    Speaker diarization model and pipeline from by https://github.com/pyannote/pyannote-audio
+def format_speaker_output_by_segment2(embeddings: np.ndarray, transcript: dict,
+                                      reference_embeddings: List[Tuple[str, np.ndarray]]) -> str:
     """
+    各発話者の埋め込みに基づいて、セグメントを整形して出力します。
+    Parameters
+    ----------
+    embeddings: np.ndarray
+        話者の埋め込みのリスト
+    transcript: dict
+        Whisper API の transcribe メソッドの出力結果
+    reference_embeddings: List[Tuple[str, np.ndarray]]
+        参照話者の名前と埋め込みのリスト
+    Returns
+    -------
+    str
+        発話者ごとに整形されたセグメントの文字列。
+    """
+    labeled_segments = []
+    for embedding, segment in zip(embeddings, transcript["segments"]):
+        speaker_name = closest_reference_speaker(embedding, reference_embeddings)
+        labeled_segments.append((speaker_name, segment["start"], segment["text"]))
+
+    output = ""
+    for speaker, _, text in labeled_segments:
+        output += f"{speaker}: 「{text}」\n"
+    return output
+
+
+import gradio as gr
+import openai
+
+
+def create_transcription_with_speaker(openai_key, main_audio, reference_audio_1, reference1_name,
+                                      reference_audio_2, reference2_name, speaker_count=2):
+    openai.api_key = openai_key
+    # 文字起こし
+    transcript = openai.Audio.transcribe("whisper-1", open(main_audio, "rb"), response_format="verbose_json")
+    # 各発話をembeddingsに変換
+    embeddings = generate_speaker_embeddings(main_audio, transcript)
+    # 各発話のembeddingsをクラスタリング
+    clustering = clustering_embeddings(speaker_count, embeddings)
+    # クラスタリングで作られた仮のラベルで各セグメントに名前付け
+    output_by_segment1 = format_speaker_output_by_segment(clustering, transcript)
+    reference1 = reference_audio_embedding(reference_audio_1)
+    reference2 = reference_audio_embedding(reference_audio_2)
+    reference_embeddings = [(reference1_name, reference1), (reference2_name, reference2)]
+    output_by_segment2 = format_speaker_output_by_segment2(embeddings, transcript, reference_embeddings)
+    return output_by_segment1, output_by_segment2
+
+
+inputs = [
+    gr.Textbox(lines=1, label="openai_key", type="password"),
+    gr.Audio(type="filepath", label="メイン音声ファイル"),
+    gr.Audio(type="filepath", label="話者 (1) 参考音声ファイル"),
+    gr.Textbox(lines=1, label="話者 (1) の名前"),
+    gr.Audio(type="filepath", label="話者 (2) 参考音声ファイル"),
+    gr.Textbox(lines=1, label="話者 (2) の名前")
+]
+
+outputs = [
+    gr.Textbox(label="話者クラスタリング文字起こし"),
+    gr.Textbox(label="話者アサイン文字起こし"),
+]
+
+app = gr.Interface(
+    fn=create_transcription_with_speaker,
+    inputs=inputs,
+    outputs=outputs,
+    title="話者アサイン機能付き書き起こしアプリ",
+    description="音声ファイルをアップロードすると、話者分離した文字起こしが作成されます。"
+)
 
-    # model = whisper.load_model(whisper_model)
-    # model = WhisperModel(whisper_model, device="cuda", compute_type="int8_float16")
-    model = WhisperModel(whisper_model, compute_type="int8")
-    time_start = time.time()
-    if (video_file_path == None):
-        raise ValueError("Error no video input")
-    print(video_file_path)
-
-    try:
-        # Read and convert youtube video
-        _, file_ending = os.path.splitext(f'{video_file_path}')
-        print(f'file enging is {file_ending}')
-        audio_file = video_file_path.replace(file_ending, ".wav")
-        print("starting conversion to wav")
-        os.system(f'ffmpeg -i "{video_file_path}" -ar 16000 -ac 1 -c:a pcm_s16le "{audio_file}"')
-
-        # Get duration
-        with contextlib.closing(wave.open(audio_file, 'r')) as f:
-            frames = f.getnframes()
-            rate = f.getframerate()
-            duration = frames / float(rate)
-        print(f"conversion to wav ready, duration of audio file: {duration}")
-
-        # Transcribe audio
-        options = dict(language=selected_source_lang, beam_size=5, best_of=5)
-        transcribe_options = dict(task="transcribe", **options)
-        segments_raw, info = model.transcribe(audio_file, **transcribe_options)
-
-        # Convert back to original openai format
-        segments = []
-        i = 0
-        for segment_chunk in segments_raw:
-            chunk = {}
-            chunk["start"] = segment_chunk.start
-            chunk["end"] = segment_chunk.end
-            chunk["text"] = segment_chunk.text
-            segments.append(chunk)
-            i += 1
-        print("transcribe audio done with fast whisper")
-    except Exception as e:
-        raise RuntimeError("Error converting video to audio")
-
-    try:
-        # Create embedding
-        def segment_embedding(segment):
-            audio = Audio()
-            start = segment["start"]
-            # Whisper overshoots the end timestamp in the last segment
-            end = min(duration, segment["end"])
-            clip = Segment(start, end)
-            waveform, sample_rate = audio.crop(audio_file, clip)
-            return embedding_model(waveform[None])
-
-        embeddings = np.zeros(shape=(len(segments), 192))
-        for i, segment in enumerate(segments):
-            embeddings[i] = segment_embedding(segment)
-        embeddings = np.nan_to_num(embeddings)
-        print(f'Embedding shape: {embeddings.shape}')
-
-        if num_speakers == 0:
-            # Find the best number of speakers
-            score_num_speakers = {}
-
-            for num_speakers in range(2, 10 + 1):
-                clustering = AgglomerativeClustering(num_speakers).fit(embeddings)
-                score = silhouette_score(embeddings, clustering.labels_, metric='euclidean')
-                score_num_speakers[num_speakers] = score
-            best_num_speaker = max(score_num_speakers, key=lambda x: score_num_speakers[x])
-            print(f"The best number of speakers: {best_num_speaker} with {score_num_speakers[best_num_speaker]} score")
-        else:
-            best_num_speaker = num_speakers
-
-        # Assign speaker label
-        clustering = AgglomerativeClustering(best_num_speaker).fit(embeddings)
-        labels = clustering.labels_
-        for i in range(len(segments)):
-            segments[i]["speaker"] = 'SPEAKER ' + str(labels[i] + 1)
-
-        # Make output
-        objects = {
-            'Start': [],
-            'End': [],
-            'Speaker': [],
-            'Text': []
-        }
-        text = ''
-        for (i, segment) in enumerate(segments):
-            if i == 0 or segments[i - 1]["speaker"] != segment["speaker"]:
-                objects['Start'].append(str(convert_time(segment["start"])))
-                objects['Speaker'].append(segment["speaker"])
-                if i != 0:
-                    objects['End'].append(str(convert_time(segments[i - 1]["end"])))
-                    objects['Text'].append(text)
-                    text = ''
-            text += segment["text"] + ' '
-        objects['End'].append(str(convert_time(segments[i - 1]["end"])))
-        objects['Text'].append(text)
-
-        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.*
-        """
-        save_path = "output/transcript_result.csv"
-        df_results = pd.DataFrame(objects)
-        df_results.to_csv(save_path)
-        return df_results, system_info, save_path
-
-    except Exception as e:
-        raise RuntimeError("Error Running inference with local model", e)
-
-
-# ---- 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)
-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*")
-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
-
-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>''')
-
-demo.launch(debug=True)
+app.launch(debug=True)