first commit

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+test.ipynb
+secrets.toml
+models/*
+models

.streamlit/config.toml

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+[theme]
+primaryColor = "#696969s"
+backgroundColor = "#000000"
+secondaryBackgroundColor = "#282828"
+textColor = "#fafafa"
+font = "sans serif"

README.md

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+---
+title: Speech To Chat
+emoji: 🐨
+colorFrom: gray
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.27.2
+app_file: app.py
+pinned: false
+---
+
+Speaker Diarization app that also has transcribing and AI Chat features.
+
+The following code is an application to perform speech diarization (the process of separating an audio stream into segments according to speaker identity) and transcription (the process of translating speech into written text). It uses both PyAnnote and Whisper APIs, and can process audio either uploaded from a local file or fetched from a YouTube video URL.
+
+## TO DO
+  - [ ] Asynchrounous Whisper requests [plan](https://stackoverflow.com/a/63179518)
+
+# References
+  - [pyannote.audio](https://github.com/pyannote/pyannote-audio)
+  - [HuggingFace pyannote diarization](https://huggingface.co/pyannote/speaker-diarization-3.0)
+  - [Whisper API](https://platform.openai.com/docs/guides/speech-to-text/quickstart)

app.py

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+import streamlit as st
+import streamlit_ext as ste
+import openai
+from pydub import AudioSegment
+from pytube import YouTube
+import pytube
+import io
+from pyannote.audio import Pipeline
+from pyannote.audio.pipelines.utils.hook import ProgressHook
+from pyannote.database.util import load_rttm
+from pyannote.core import Annotation, Segment, notebook
+import time
+import json
+import torch
+import urllib.parse as urlparse
+from urllib.parse import urlencode
+import os
+
+import matplotlib
+matplotlib.use('Agg')
+from matplotlib import pyplot as plt
+
+st.set_page_config(
+        page_title="Speech-to-chat",
+        page_icon = '🌊'
+)
+
+def create_audio_stream(audio):
+    return io.BytesIO(audio.export(format="wav").read())
+
+def add_query_parameter(link, params):
+    url_parts = list(urlparse.urlparse(link))
+    query = dict(urlparse.parse_qsl(url_parts[4]))
+    query.update(params)
+
+    url_parts[4] = urlencode(query)
+
+    return urlparse.urlunparse(url_parts)
+
+def youtube_video_id(value):
+    """
+    Examples:
+    - http://youtu.be/SA2iWivDJiE
+    - http://www.youtube.com/watch?v=_oPAwA_Udwc&feature=feedu
+    - http://www.youtube.com/embed/SA2iWivDJiE
+    - http://www.youtube.com/v/SA2iWivDJiE?version=3&hl=en_US
+    """
+    query = urlparse.urlparse(value)
+    if query.hostname == 'youtu.be':
+        return query.path[1:]
+    if query.hostname in ('www.youtube.com', 'youtube.com'):
+        if query.path == '/watch':
+            p = urlparse.parse_qs(query.query)
+            return p['v'][0]
+        if query.path[:7] == '/embed/':
+            return query.path.split('/')[2]
+        if query.path[:3] == '/v/':
+            return query.path.split('/')[2]
+    # fail?
+    return None
+
+
+def load_rttm_file(rttm_path):
+    return load_rttm(rttm_path)['stream']
+
+
+def load_audio(uploaded_audio):
+    return AudioSegment.from_file(uploaded_audio)
+
+
+# Set your OpenAI, Hugging Face API keys
+openai.api_key = os.getenv('openai')
+hf_api_key = os.getenv('hf')
+
+st.title("Speech Diarization and Speech-to-Text with PyAnnote and Whisper")
+reddit_thread = 'https://www.reddit.com/r/dataisbeautiful/comments/17413bq/oc_speech_diarization_app_that_transcribes_audio'
+with st.expander('About', expanded=True):
+    st.markdown(f'''
+        Given an audio file this app will
+          - [x] 1. Identify and diarize the speakers using `pyannote` [HuggingFace Speaker Diarization api](https://huggingface.co/pyannote/speaker-diarization-3.0)
+          - [x] 2. Transcribe the audio and attribute to speakers using [OpenAi Whisper API](https://platform.openai.com/docs/guides/speech-to-text/quickstart)
+          - [ ] 3. Set up an LLM chat with the transcript loaded into its knowledge database, so that a user can "talk" to the transcript of the audio file (WIP)
+
+        This version will only process up to first 6 minutes of an audio file due to limited resources of Streamlit.io apps.
+        A local version with access to a GPU can process 1 hour of audio in 1 to 5 minutes.
+        If you would like to use this app at scale reach out directly by creating an issue on github [🤖](https://github.com/KobaKhit/speech-to-text-app/issues)!
+        
+        Rule of thumb, for this Streamlit.io hosted app it takes half the duration of the audio to complete processing, ex. g. 6 minute youtube video will take 3 minutes to diarize.
+
+        [github repo](https://github.com/KobaKhit/speech-to-text-app)
+    ''')
+
+
+option = st.radio("Select source:", ["Upload an audio file", "Use YouTube link","See Example"], index=2)
+
+# Upload audio file
+if option == "Upload an audio file":
+    uploaded_audio = st.file_uploader("Upload an audio file (MP3 or WAV)", type=["mp3", "wav","mp4"])
+    with st.expander('Optional Parameters'):
+        rttm = st.file_uploader("Upload .rttm if you already have one", type=["rttm"])
+        transcript_file = st.file_uploader("Upload transcipt json", type=["json"])
+        youtube_link = st.text_input('Youtube link of the audio sample')
+
+    if uploaded_audio is not None:
+        st.audio(uploaded_audio, format="audio/wav", start_time=0)
+        audio_name = uploaded_audio.name
+        audio = load_audio(uploaded_audio)
+        
+        # sample_rate = st.number_input("Enter the sample rate of the audio", min_value=8000, max_value=48000)
+        # audio = audio.set_frame_rate(sample_rate)
+        
+# use youtube link
+elif option == "Use YouTube link":
+    
+    youtube_link_raw = st.text_input("Enter the YouTube video URL:")
+    youtube_link = f'https://youtu.be/{youtube_video_id(youtube_link_raw)}'
+
+    with st.expander('Optional Parameters'):
+        rttm = st.file_uploader("Upload .rttm if you already have one", type=["rttm"])
+        transcript_file = st.file_uploader("Upload transcipt json", type=["json"])  
+    if youtube_link_raw:
+        st.write(f"Fetching audio from YouTube: {youtube_link}")
+        try:
+            yt = YouTube(youtube_link)
+            audio_stream = yt.streams.filter(only_audio=True).first()
+            audio_name = audio_stream.default_filename
+            st.write(f"Downloaded {audio_name}")
+        except pytube.exceptions.AgeRestrictedError:
+            st.stop('Age restricted videos cannot be processed.')
+
+        try:
+            os.remove('sample.mp4')
+        except OSError:
+            pass
+        audio_file = audio_stream.download(filename='sample.mp4')
+        time.sleep(2)
+        audio = load_audio('sample.mp4')
+        st.audio(create_audio_stream(audio), format="audio/mp4", start_time=0)
+        # sample_rate = st.number_input("Enter the sample rate of the audio", min_value=8000, max_value=48000)
+        # audio = audio.set_frame_rate(sample_rate)
+        # except Exception as e:
+        #     st.write(f"Error: {str(e)}")
+elif option == 'See Example':
+    youtube_link = 'https://www.youtube.com/watch?v=TamrOZX9bu8'
+    audio_name = 'Stephen A. Smith has JOKES with Shannon Sharpe'
+    st.write(f'Loaded audio file from {youtube_link} - Stephen A. Smith has JOKES with Shannon Sharpe 👏😂')
+    if os.path.isfile('example/steve a smith jokes.mp4'):
+        audio = load_audio('example/steve a smith jokes.mp4')
+    else:
+        yt = YouTube(youtube_link)
+        audio_stream = yt.streams.filter(only_audio=True).first()
+        audio_file = audio_stream.download(filename='sample.mp4')
+        time.sleep(2)
+        audio = load_audio('sample.mp4')
+
+    if os.path.isfile("example/steve a smith jokes.rttm"):
+        rttm = "example/steve a smith jokes.rttm"
+    if os.path.isfile('example/steve a smith jokes.json'):
+        transcript_file = 'example/steve a smith jokes.json'
+
+    st.audio(create_audio_stream(audio), format="audio/mp4", start_time=0)
+    
+                                    
+
+# Diarize
+if "audio" in locals():
+    st.write('Performing Diarization...')
+    # create stream
+    duration = audio.duration_seconds
+    if duration > 360:
+        st.info('Only processing the first 6 minutes of the audio due to Streamlit.io resource limits.')
+        audio = audio[:360*1000]
+        duration = audio.duration_seconds
+    
+    
+    # Perform diarization with PyAnnote
+    # "pyannote/speaker-diarization-3.0",
+    # use_auth_token=hf_api_key
+    pipeline = Pipeline.from_pretrained(
+       "pyannote/speaker-diarization-3.0", use_auth_token=hf_api_key)
+    if torch.cuda.device_count() > 0: # use gpu if available
+        pipeline.to(torch.device('cuda'))
+
+    # run the pipeline on an audio file
+    if 'rttm' in locals() and rttm != None:
+        st.write(f'Loading {rttm}')
+        diarization = load_rttm_file(rttm)
+    else:
+        # with ProgressHook() as hook:
+        audio_ = create_audio_stream(audio)
+        # diarization = pipeline(audio_, hook=hook)
+        diarization = pipeline(audio_)
+        # dump the diarization output to disk using RTTM format
+        with open(f'{audio_name.split(".")[0]}.rttm', "w") as f:
+            diarization.write_rttm(f)
+    
+    # Display the diarization results
+    st.write("Diarization Results:")
+    
+    annotation = Annotation()
+    sp_chunks = []
+    progress_text = f"Processing 1/{len(sp_chunks)}..."
+    my_bar = st.progress(0, text=progress_text)
+    counter = 0
+    n_tracks = len([a for a in diarization.itertracks(yield_label=True)])
+    for turn, _, speaker in diarization.itertracks(yield_label=True):
+        annotation[turn] = speaker
+        progress_text = f"Processing {counter}/{len(sp_chunks)}..."
+        my_bar.progress((counter+1)/n_tracks, text=progress_text)
+        counter +=1
+        temp = {'speaker': speaker,
+                'start': turn.start, 'end': turn.end, 'duration': turn.end-turn.start,
+                'audio': audio[turn.start*1000:turn.end*1000]}
+        if 'transcript_file' in locals() and transcript_file == None:
+            temp['audio_stream'] = create_audio_stream(audio[turn.start*1000:turn.end*1000])
+        sp_chunks.append(temp)
+
+    # plot
+    notebook.crop = Segment(-1, duration + 1)
+    figure, ax = plt.subplots(figsize=(10,3))
+    notebook.plot_annotation(annotation, ax=ax, time=True, legend=True)
+    figure.tight_layout()
+    # save to file
+    st.pyplot(figure)
+
+    st.write('Speakers and Audio Samples')
+    with st.expander('Samples', expanded=True):
+        for speaker in set(s['speaker'] for s in sp_chunks):
+            temp = max(filter(lambda d: d['speaker'] == speaker, sp_chunks), key=lambda x: x['duration'])
+            speak_time = sum(c['duration'] for c in filter(lambda d: d['speaker'] == speaker, sp_chunks))
+            rate = 100*min((speak_time, duration))/duration
+            speaker_summary  = f"{temp['speaker']} ({round(rate)}% of video duration): start={temp['start']:.1f}s stop={temp['end']:.1f}s"
+            if youtube_link != None:
+                speaker_summary += f" {add_query_parameter(youtube_link, {'t':str(int(temp['start']))})}"
+            st.write(speaker_summary)
+            st.audio(create_audio_stream(temp['audio']))
+
+
+    # st.write("Transcription with Whisper ASR:")
+    
+    st.divider()
+    # # Perform transcription with Whisper ASR
+    st.write('Transcribing using Whisper API (150 requests limit)...')
+    container = st.container()
+
+    limit = 150
+    progress_text = f"Processing 1/{len(sp_chunks[:limit])}..."
+    my_bar = st.progress(0, text=progress_text)
+    with st.expander('Transcript', expanded=True):
+        if 'transcript_file' in locals() and transcript_file != None:
+            with open(transcript_file,'r') as f:
+                sp_chunks_loaded = json.load(f)
+            for i,s in enumerate(sp_chunks_loaded):
+                if s['transcript'] != None:
+                    transcript_summary = f"{s['speaker']} start={float(s['start']):.1f}s end={float(s['end']):.1f}s: {s['transcript']}" 
+                    if youtube_link != None:
+                        transcript_summary += f" {add_query_parameter(youtube_link, {'t':str(int(s['start']))})}"
+
+                    st.write(transcript_summary)
+                progress_text = f"Processing {i+1}/{len(sp_chunks_loaded)}..."
+                my_bar.progress((i+1)/len(sp_chunks_loaded), text=progress_text)
+
+            transcript_json = sp_chunks_loaded
+            transcript_path = f'example-transcript.json'
+
+        else:
+            sp_chunks_updated = []
+            for i,s in enumerate(sp_chunks[:limit]):
+                if s['duration'] > 0.1:
+                    audio_path = s['audio'].export('temp.wav',format='wav')
+                    try:
+                        transcript = openai.Audio.transcribe("whisper-1", audio_path)['text']
+                    except Exception:
+                        transcript = ''
+                        pass
+
+                    if transcript !='' and transcript != None:
+                        s['transcript'] = transcript
+                        transcript_summary = f"{s['speaker']} start={s['start']:.1f}s end={s['end']:.1f}s : {s['transcript']}" 
+                        if youtube_link != None:
+                            transcript_summary += f" {add_query_parameter(youtube_link, {'t':str(int(s['start']))})}"
+                        
+                        sp_chunks_updated.append({'speaker':s['speaker'], 
+                                                'start':s['start'], 'end':s['end'],
+                                                'duration': s['duration'],'transcript': transcript})
+
+                        progress_text = f"Processing {i+1}/{len(sp_chunks[:limit])}..."
+                        my_bar.progress((i+1)/len(sp_chunks[:limit]), text=progress_text)
+                        st.write(transcript_summary)
+
+            transcript_json = [dict((k, d[k]) for k in ['speaker','start','end','duration','transcript'] if k in d) for d in sp_chunks_updated]
+            transcript_path = f'{audio_name.split(".")[0]}-transcript.json'
+
+    with open(transcript_path,'w') as f:
+        json.dump(transcript_json, f)
+    
+    with container:
+        st.info(f'Completed transcribing')
+       
+        @st.cache_data
+        def convert_df(string):
+            # IMPORTANT: Cache the conversion to prevent computation on every rerun
+            return string.encode('utf-8')
+
+        transcript_json_download = convert_df(json.dumps(transcript_json))
+
+        c1_b,c2_b = st.columns((1,2))
+        with c1_b:
+            ste.download_button(
+                "Download transcript as json",
+                transcript_json_download,
+                transcript_path,
+            )
+
+        header = ','.join(transcript_json[0].keys()) + '\n'
+        for s in transcript_json:
+            header += ','.join([str(e) if ',' not in str(e) else '"' + str(e) + '"' for e in s.values()]) + '\n'
+
+        transcript_csv_download = convert_df(header)
+        with c2_b:
+            ste.download_button(
+                "Download transcript as csv",
+                transcript_csv_download,
+                f'{audio_name.split(".")[0]}-transcript.csv'
+            )
+    

example/steve a smith jokes.json

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+[{"speaker": "SPEAKER_02", "start": 1.0101867572156198, "end": 17.173174872665534, "duration": 16.162988115449913, "transcript": "You can call it day two but it's really day one because the full two-hour show with me and the man Shannon Sharpless. I know it was Hiccup last year, I know it was Hiccup yesterday Shannon, I know it was Hiccup yesterday. Alright, I mean, cause you called me something other than Stephen A, but I got a solution to the problem. "}, {"speaker": "SPEAKER_01", "start": 12.589134125636674, "end": 13.607809847198643, "duration": 1.0186757215619693, "transcript": "I don't think I'm going to stay forever."}, {"speaker": "SPEAKER_02", "start": 18.3276740237691, "end": 19.41426146010187, "duration": 1.0865874363327706, "transcript": "Ah, there we go!"}, {"speaker": "SPEAKER_00", "start": 19.41426146010187, "end": 19.92359932088285, "duration": 0.5093378607809811, "transcript": "Yo!"}, {"speaker": "SPEAKER_02", "start": 19.92359932088285, "end": 20.04244482173175, "duration": 0.11884550084889867, "transcript": "You"}, {"speaker": "SPEAKER_00", "start": 20.449915110356535, "end": 25.679117147707984, "duration": 5.229202037351449, "transcript": "What's up, y'all? My name is Steven A. What's up, baby? And you are Molly Carol."}, {"speaker": "SPEAKER_03", "start": 25.679117147707984, "end": 28.599320882852297, "duration": 2.9202037351443124, "transcript": "Yes, you said it right, day two."}, {"speaker": "SPEAKER_00", "start": 26.35823429541596, "end": 26.56196943972835, "duration": 0.2037351443123896, "transcript": "Thank you."}, {"speaker": "SPEAKER_00", "start": 28.3616298811545, "end": 30.11035653650255, "duration": 1.748726655348051, "transcript": "It only took me seven years. You know what I..."}, {"speaker": "SPEAKER_03", "start": 29.38030560271647, "end": 34.03225806451613, "duration": 4.6519524617996595, "transcript": "Stephen A is still learning. Karam, good job. It rhymes with harem. Karam. "}, {"speaker": "SPEAKER_01", "start": 31.876061120543298, "end": 32.826825127334466, "duration": 0.950764006791168, "transcript": "Good job. It rhymes with."}, {"speaker": "SPEAKER_01", "start": 33.09847198641766, "end": 40.36502546689304, "duration": 7.26655348047538, "transcript": "She said it rhymes with harem. We just called you cute. She had to tell the world it rhymes with harem? I'm just wondering."}, {"speaker": "SPEAKER_03", "start": 37.68251273344652, "end": 38.1578947368421, "duration": 0.47538200339558045, "transcript": "because she had."}, {"speaker": "SPEAKER_03", "start": 40.36502546689304, "end": 41.34974533106961, "duration": 0.9847198641765758, "transcript": "That's what it rhymes with."}, {"speaker": "SPEAKER_02", "start": 42.74193548387097, "end": 49.278438030560274, "duration": 6.536502546689306, "transcript": "Thanks for watching ESPN on YouTube. For live streaming sports and premium content, subscribe to ESPN Plus."}]

example/steve a smith jokes.rttm

@@ -0,0 +1,34 @@
+SPEAKER stream 1 1.010 16.163 <NA> <NA> SPEAKER_02 <NA> <NA>
+SPEAKER stream 1 12.589 1.019 <NA> <NA> SPEAKER_01 <NA> <NA>
+SPEAKER stream 1 18.328 1.087 <NA> <NA> SPEAKER_02 <NA> <NA>
+SPEAKER stream 1 19.414 0.509 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 19.924 0.119 <NA> <NA> SPEAKER_02 <NA> <NA>
+SPEAKER stream 1 20.093 0.068 <NA> <NA> SPEAKER_02 <NA> <NA>
+SPEAKER stream 1 20.331 0.119 <NA> <NA> SPEAKER_02 <NA> <NA>
+SPEAKER stream 1 20.450 5.229 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 22.759 0.187 <NA> <NA> SPEAKER_02 <NA> <NA>
+SPEAKER stream 1 22.997 0.102 <NA> <NA> SPEAKER_02 <NA> <NA>
+SPEAKER stream 1 25.679 2.920 <NA> <NA> SPEAKER_03 <NA> <NA>
+SPEAKER stream 1 26.358 0.204 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 28.362 1.749 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 29.380 4.652 <NA> <NA> SPEAKER_03 <NA> <NA>
+SPEAKER stream 1 30.110 0.051 <NA> <NA> SPEAKER_01 <NA> <NA>
+SPEAKER stream 1 30.161 0.034 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 30.195 0.034 <NA> <NA> SPEAKER_01 <NA> <NA>
+SPEAKER stream 1 30.229 0.034 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 30.263 0.017 <NA> <NA> SPEAKER_01 <NA> <NA>
+SPEAKER stream 1 30.280 0.017 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 31.146 0.340 <NA> <NA> SPEAKER_01 <NA> <NA>
+SPEAKER stream 1 31.486 0.119 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 31.825 0.051 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 31.876 0.951 <NA> <NA> SPEAKER_01 <NA> <NA>
+SPEAKER stream 1 33.098 7.267 <NA> <NA> SPEAKER_01 <NA> <NA>
+SPEAKER stream 1 35.458 0.017 <NA> <NA> SPEAKER_03 <NA> <NA>
+SPEAKER stream 1 35.475 0.051 <NA> <NA> SPEAKER_00 <NA> <NA>
+SPEAKER stream 1 35.526 0.051 <NA> <NA> SPEAKER_03 <NA> <NA>
+SPEAKER stream 1 36.545 0.085 <NA> <NA> SPEAKER_02 <NA> <NA>
+SPEAKER stream 1 37.632 0.051 <NA> <NA> SPEAKER_02 <NA> <NA>
+SPEAKER stream 1 37.683 0.475 <NA> <NA> SPEAKER_03 <NA> <NA>
+SPEAKER stream 1 40.229 0.085 <NA> <NA> SPEAKER_03 <NA> <NA>
+SPEAKER stream 1 40.365 0.985 <NA> <NA> SPEAKER_03 <NA> <NA>
+SPEAKER stream 1 42.742 6.537 <NA> <NA> SPEAKER_02 <NA> <NA>

packages.txt

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

requirements.txt

@@ -0,0 +1,10 @@
+openai==0.28.1
+openai-whisper==20230918
+pydub==0.25.1
+pytube==15.0.0
+streamlit==1.27.2
+streamlit-ext==0.1.8
+ffmpeg==1.4
+pyannote.audio==3.0.1
+pyannote.core==5.0.0
+