Update app.py

app.py

@@ -1,69 +1,381 @@
+# 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
 
-    # 音声ファイルを開く
-    audio = AudioSegment.from_file(uploaded_audio)
+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"
 
-    # 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]
+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"
+        )
 
-    # 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)
+    elif (microphone is None) and (file_upload is None):
+        return "ERROR: You have to either use the microphone or upload an audio file"
 
-    # 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")
+    file = microphone if microphone is not None else file_upload
 
-        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"
+    text = pipe(file)["text"]
 
-    system_template = prompt
+    return warn_output + text
 
-    completion = openai.ChatCompletion.create(
-        model="gpt-3.5-turbo",
-        messages=[
-            {"role": "system", "content": system_template},
-            {"role": "user", "content": transcript_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>"
     )
-    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。"
-)
+    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")
+
 
-app.launch(debug=True)
+demo.launch(debug=True)

requirements.txt

@@ -1,5 +1,3 @@
-openai==0.27.2
-pydub==0.25.1
 git+https://github.com/huggingface/transformers
 git+https://github.com/pyannote/pyannote-audio
 git+https://github.com/openai/whisper.git
@@ -21,4 +19,5 @@ psutil==5.9.2
 requests
 gpuinfo
 faster-whisper
-yt-dlp
+yt-dlp
+openai==0.27.2