Delete app.py

app.py

@@ -1,118 +0,0 @@
-from collections import deque
-import streamlit as st
-import torch
-from streamlit_player import st_player
-from transformers import AutoModelForCTC, Wav2Vec2Processor
-from streaming import ffmpeg_stream
-
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-player_options = {
-    "events": ["onProgress"],
-    "progress_interval": 200,
-    "volume": 1.0,
-    "playing": True,
-    "loop": False,
-    "controls": False,
-    "muted": False,
-    "config": {"youtube": {"playerVars": {"start": 1}}},
-}
-
-# disable rapid fading in and out on `st.code` updates
-st.markdown("<style>.element-container{opacity:1 !important}</style>", unsafe_allow_html=True)
-
-@st.cache(hash_funcs={torch.nn.parameter.Parameter: lambda _: None})
-def load_model(model_path="facebook/wav2vec2-large-robust-ft-swbd-300h"):
-    processor = Wav2Vec2Processor.from_pretrained(model_path)
-    model = AutoModelForCTC.from_pretrained(model_path).to(device)
-    return processor, model
-    
-processor, model = load_model()
-
-def stream_text(url, chunk_duration_ms, pad_duration_ms):
-    sampling_rate = processor.feature_extractor.sampling_rate
-
-    # calculate the length of logits to cut from the sides of the output to account for input padding
-    output_pad_len = model._get_feat_extract_output_lengths(int(sampling_rate * pad_duration_ms / 1000))
-
-    # define the audio chunk generator
-    stream = ffmpeg_stream(url, sampling_rate, chunk_duration_ms=chunk_duration_ms, pad_duration_ms=pad_duration_ms)
-
-    leftover_text = ""
-    for i, chunk in enumerate(stream):
-        input_values = processor(chunk, sampling_rate=sampling_rate, return_tensors="pt").input_values
-
-        with torch.no_grad():
-            logits = model(input_values.to(device)).logits[0]
-            if i > 0:
-                logits = logits[output_pad_len : len(logits) - output_pad_len]
-            else:  # don't count padding at the start of the clip
-                logits = logits[: len(logits) - output_pad_len]
-
-            predicted_ids = torch.argmax(logits, dim=-1).cpu().tolist()
-            if processor.decode(predicted_ids).strip():
-                leftover_ids = processor.tokenizer.encode(leftover_text)
-                # concat the last word (or its part) from the last frame with the current text
-                text = processor.decode(leftover_ids + predicted_ids)
-                # don't return the last word in case it's just partially recognized
-                text, leftover_text = text.rsplit(" ", 1)
-                yield text
-            else:
-                yield leftover_text
-                leftover_text = ""
-    yield leftover_text
-
-def main():
-    state = st.session_state
-    st.header("Video ASR Streamlit from Youtube Link")
-
-    with st.form(key="inputs_form"):
-    
-        # Our worlds best teachers on subjects of AI, Cognitive, Neuroscience for our Behavioral and Medical Health
-        ytSeanKelly="https://youtu.be/cC1HszE5Hcw?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=8984"
-        ytSamHarris="https://www.youtube.com/watch?v=4dC_nRYIDZU&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=2"
-        ytJohnAbramson="https://www.youtube.com/watch?v=arrokG3wCdE&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=3"
-        ytElonMusk="https://www.youtube.com/watch?v=DxREm3s1scA&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=4"
-        ytJeffreyShainline="https://www.youtube.com/watch?v=EwueqdgIvq4&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=5"
-        ytJeffHawkins="https://www.youtube.com/watch?v=Z1KwkpTUbkg&list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&index=6"
-        ytSamHarris="https://youtu.be/Ui38ZzTymDY?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L"
-        ytSamHarris="https://youtu.be/4dC_nRYIDZU?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=7809"
-        ytSamHarris="https://youtu.be/4dC_nRYIDZU?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=7809"
-        ytSamHarris="https://youtu.be/4dC_nRYIDZU?list=PLHgX2IExbFouJoqEr8JMF5MbZSbyC91-L&t=7809"
-        state.youtube_url = st.text_input("YouTube URL", ytSeanKelly)
-        
-        
-        state.chunk_duration_ms = st.slider("Audio chunk duration (ms)", 2000, 10000, 3000, 100)
-        state.pad_duration_ms = st.slider("Padding duration (ms)", 100, 5000, 1000, 100)
-        submit_button = st.form_submit_button(label="Submit")
-
-    if submit_button or "asr_stream" not in state:
-        # a hack to update the video player on value changes
-        state.youtube_url = (
-            state.youtube_url.split("&hash=")[0]
-            + f"&hash={state.chunk_duration_ms}-{state.pad_duration_ms}"
-        )
-        state.asr_stream = stream_text(
-            state.youtube_url, state.chunk_duration_ms, state.pad_duration_ms
-        )
-        state.chunks_taken = 0
-        
-        
-        state.lines = deque([], maxlen=100)  # limit to the last n lines of subs
-        
-
-    player = st_player(state.youtube_url, **player_options, key="youtube_player")
-
-    if "asr_stream" in state and player.data and player.data["played"] < 1.0:
-        # check how many seconds were played, and if more than processed - write the next text chunk
-        processed_seconds = state.chunks_taken * (state.chunk_duration_ms / 1000)
-        if processed_seconds < player.data["playedSeconds"]:
-            text = next(state.asr_stream)
-            state.lines.append(text)
-            state.chunks_taken += 1
-    if "lines" in state:
-        # print the lines of subs
-        st.code("\n".join(state.lines))
-
-
-if __name__ == "__main__":
-    main()