init repo

app.py

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+import whisper
+import datetime
+import subprocess
+import gradio as gr
+from pathlib import Path
+import pandas as pd
+import re
+import time
+import os 
+import numpy as np
+from sklearn.cluster import AgglomerativeClustering
+import base64
+
+from pytube import YouTube
+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
+
+import wave
+import contextlib
+from transformers import pipeline
+import psutil
+
+# os.system('git clone https://github.com/ggerganov/whisper.cpp.git')
+# os.system('make -C ./whisper.cpp')
+# os.system('bash ./whisper.cpp/models/download-ggml-model.sh base')
+
+
+whisper_models = ["base", "small", "medium", "large"]
+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 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")
+
+    text = pipe("audio.mp3")["text"]
+
+    return html_embed_str, text
+
+#format a float as "00"."000"
+format_float = lambda x: '{:.3f}'.format(x)
+
+def convert_time(secs):
+    
+    td=str(datetime.timedelta(seconds=secs))
+    h,m,s = re.split(':', td)
+    #format float as 2 digits before decimal and 3 digits after
+    time=str(h)+':'+str(m)+':'+'{:06.3f}'.format(float(s))
+    # print(time)
+    return time
+
+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()
+    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,device):
+    """
+    # 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.
+    
+    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
+    """
+
+    if device=="gpu" and torch.cuda.is_available():
+        device = torch.device("cuda")
+    elif device=="cpu":
+        device = torch.device("cpu")
+    elif device=="gpu" and not torch.cuda.is_available():
+        raise ValueError("Error: GPU not available")
+    
+    print("device is ", device)
+    
+    embedding_model = PretrainedSpeakerEmbedding( 
+        "speechbrain/spkrec-ecapa-voxceleb",
+        device=device)
+    
+    model = whisper.load_model(whisper_model, device=device)
+    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 ending 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)
+        result = model.transcribe(audio_file, **transcribe_options)
+        segments = result["segments"]
+        print("starting whisper done with whisper")
+        print(segments[0])
+    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}')
+
+        # Assign speaker label
+        clustering = AgglomerativeClustering(num_speakers).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):
+            
+            objects['Start'].append(convert_time(segment["start"]))
+            objects['Speaker'].append(segment["speaker"])
+
+
+            text += segment["text"] + ' '
+            objects['End'].append(str(convert_time(segment["end"])))
+            objects['Text'].append(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)
+        #save an srt file from df_results
+        srt_file = "output/subtitles.srt"
+        def to_srt(df, srt_file):
+            with open(srt_file, 'w') as f:
+                for i, row in df.iterrows():
+                    f.write(f'{i + 1}\n')
+
+                    f.write(f'{row["Start"]} --> {row["End"]}\n')
+                    f.write(f'{row["Speaker"]} : {row["Text"]}\n\n')
+        to_srt(df_results, srt_file)
+        def to_vtt(df, vtt_file):
+            with open(vtt_file, 'w') as f:
+                f.write("WEBVTT\n\n")
+                for i, row in df.iterrows():
+                    f.write(f'{i + 1}\n')
+                    f.write(f'{row["Start"]} --> {row["End"]}\n')
+                    f.write(f'{row["Speaker"]} : {row["Text"]}\n\n')
+        vtt_file = "output/subtitles.vtt"
+        to_vtt(df_results, vtt_file)
+
+
+        return df_results, system_info, save_path, srt_file,vtt_file
+    
+    except Exception as e:
+        raise RuntimeError("Error Running inference with local model", e)
+
+def create_video_player(subtitle_files, video_in):
+
+    with open(video_in, "rb") as file:
+        video_base64 = base64.b64encode(file.read())
+    with open('output/subtitles.vtt', "rb") as file:
+        subtitle_base64 = base64.b64encode(file.read())
+
+    video_player = f'''<video id="video" controls preload="metadata">
+      <source src="data:video/mp4;base64,{str(video_base64)[2:-1]}" type="video/mp4" />
+      <track
+        label="English"
+        kind="subtitles"
+        srclang="en"
+        src="data:text/vtt;base64,{str(subtitle_base64)[2:-1]}"
+        default />
+    </video>
+    '''
+    #video_player = gr.HTML(video_player)
+    return video_player
+
+# def create_video_player(subtitle_file, video_in):
+#     video_player=gr.Video(
+#         label="Video File Test",
+#         show_label=True,
+#         interactive=True,
+#         value="mp4/en.mp4",
+#         caption="tmp/en.vtt",
+#     )
+# def add_subtitles(video_in, subtitle_file):
+#     video_player = gr.Video(
+#         label="Video File Test",
+#         show_label=True,
+#         interactive=True,
+#         value=video_in,
+#         caption=subtitle_file,
+#     )
+#     return video_player
+
+# ---- 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)
+video_out = gr.Video(label="Video Out", mirror_webcam=False)
+
+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="en", label="Spoken language in video", interactive=True)
+selected_device = gr.Dropdown(choices=['cpu','gpu'], type="value", value="cpu", label="Device on which to perform the computations.", 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=2, label="Selected 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")
+download_srt = gr.File(label="Download .srt file")
+download_vtt = gr.File(label="Download .vtt file")
+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
+video_player = gr.HTML('<p>video will be played here after you press the button at step 4')
+
+
+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> to recoginze the speech and ECAPA-TDNN model from <a href='https://github.com/speechbrain/speechbrain' target='_blank'><b>SpeechBrain</b></a> to encode and clasify speakers</h2>
+            It is based on https://huggingface.co/spaces/vumichien/Whisper_speaker_diarization 
+            </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=guEyxTpevFo", 
+                  "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 should select a number of speakers for getting better results.
+                    ''')
+                selected_device.render()
+                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,selected_device], [transcription_df, system_info, download_transcript, download_srt,download_vtt])
+
+                
+        with gr.Row():
+            gr.Markdown('''
+            ##### Here you will get transcription  output
+            ##### ''')
+            
+
+        with gr.Row():
+            with gr.Column():
+                download_transcript.render()
+                download_srt.render()
+                download_vtt.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.Row():
+            with gr.Column():
+                gr.Markdown('''
+                ##### Now press the Step 4. Button to create output video with translated transcriptions
+                ##### ''')
+                create_video_button = gr.Button("Step 4. Create and add subtitles to video")
+                print(video_in)
+                create_video_button.click(create_video_player, [download_srt,video_in], [
+                    video_player])
+                video_player.render()
+
+                
+    
+    # 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)

packages.txt

@@ -0,0 +1 @@
+ffmpeg

requirements.txt

@@ -0,0 +1,20 @@
+git+https://github.com/huggingface/transformers
+git+https://github.com/pyannote/pyannote-audio
+git+https://github.com/openai/whisper.git
+gradio==3.12
+ffmpeg-python
+pandas==1.5.0
+pytube==12.1.0
+sacremoses
+sentencepiece
+tokenizers
+torch
+torchaudio
+tqdm==4.64.1
+EasyNMT==2.0.2   
+nltk
+transformers
+pysrt
+psutil==5.9.2
+requests
+gpuinfo