Update app.py

app.py

@@ -1,381 +1,69 @@
-# 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 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
-from pyannote.core import Segment
-
-from gpuinfo import GPUInfo
-
-import wave
-import contextlib
-from transformers import pipeline
-import psutil
-
 import openai
 import os
+from io import BytesIO
 import tempfile
 from pydub import AudioSegment
+import shutil
 
+def create_meeting_summary(openai_key, prompt, uploaded_audio, max_transcribe_seconds):
+    openai.api_key = openai_key
 
-whisper_models = ["tiny", "base", "small", "medium", "large-v1", "large-v2"]
-source_languages = {
-    "en": "English",
-    "ja": "Japanese",
-}
-
-source_language_list = [key[0] for key in source_languages.items()]
-
-MODEL_NAME = "vumichien/whisper-medium-jp"
-lang = "ja"
+    # 音声ファイルを開く
+    audio = AudioSegment.from_file(uploaded_audio)
 
-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")
-
-embedding_model = PretrainedSpeakerEmbedding(
-    "speechbrain/spkrec-ecapa-voxceleb",
-    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"
-        )
+    # 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]
 
-    elif (microphone is None) and (file_upload is None):
-        return "ERROR: You have to either use the microphone or upload an audio file"
+    # 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)
 
-    file = microphone if microphone is not None else file_upload
+    # 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")
 
-    text = pipe(file)["text"]
+        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"
 
-    return warn_output + text
+    system_template = prompt
 
-
-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>"
+    completion = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": system_template},
+            {"role": "user", "content": transcript_text}
+        ]
     )
-    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
-
-
-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 speech_to_text(video_file_path, selected_source_lang, whisper_model, num_speakers):
-    """
-    # 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
-    """
-
-    # 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)
-
-
-# def create_meeting_summary(openai_key, prompt):
-#     openai.api_key = openai_key
-#
-#     # 文字起こししたテキストを取得
-#     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
-
-
-# ---- 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>''')
-
-        # with gr.Row():
-        #     with gr.Column():
-        #         gr.Textbox(lines=1, label="openai_key", type="password")
-        #         gr.TextArea(label="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.Textbox(label="transcription_summary")
-
+    summary = completion.choices[0].message.content
+    return summary, transcript_text
+
+
+inputs = [
+    gr.Textbox(lines=1, label="openai_key", type="password"),
+    gr.TextArea(label="プロンプト", 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"),
+    gr.Textbox(lines=1, label="最大文字起こし時間（秒）", type="text"),
+]
+
+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。"
+)
 
-demo.launch(debug=True)
+app.launch(debug=True)