whisper demo added

app.py

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+import whisper
+import torch
+import torchaudio
+import streamlit as st
+
+LANGUAGES = {
+    "english":"en",
+    "chinese":"zh",
+    "german":"de",
+    "spanish":"es",
+    "russian":"ru",
+    "korean":"ko",
+    "french":"fr",
+    "japanese":"ja",
+    "portuguese":"pt",
+    "turkish":"tr",
+    "polish":"pl",
+    "catalan":"ca",
+    "dutch":"nl",
+    "arabic":"ar",
+    "swedish":"sv",
+    "italian":"it",
+    "indonesian":"id",
+    "hindi":"hi",
+    "finnish":"fi",
+    "vietnamese":"vi",
+    "hebrew":"iw",
+    "ukrainian":"uk",
+    "greek":"el",
+    "malay":"ms",
+    "czech":"cs",
+    "romanian":"ro",
+    "danish":"da",
+    "hungarian":"hu",
+    "tamil":"ta",
+    "norwegian":"no",
+    "thai":"th",
+    "urdu":"ur",
+    "croatian":"hr",
+    "bulgarian":"bg",
+    "lithuanian":"lt",
+    "latin":"la",
+    "maori":"mi",
+    "malayalam":"ml",
+    "welsh":"cy",
+    "slovak":"sk",
+    "telugu":"te",
+    "persian":"fa",
+    "latvian":"lv",
+    "bengali":"bn",
+    "serbian":"sr",
+    "azerbaijani":"az",
+    "slovenian":"sl",
+    "kannada":"kn",
+    "estonian":"et",
+    "macedonian":"mk",
+    "breton":"br",
+    "basque":"eu",
+    "icelandic":"is",
+    "armenian":"hy",
+    "nepali":"ne",
+    "mongolian":"mn",
+    "bosnian":"bs",
+    "kazakh":"kk",
+    "albanian":"sq",
+    "swahili":"sw",
+    "galician":"gl",
+    "marathi":"mr",
+    "punjabi":"pa",
+    "sinhala":"si",
+    "khmer":"km",
+    "shona":"sn",
+    "yoruba":"yo",
+    "somali":"so",
+    "afrikaans":"af",
+    "occitan":"oc",
+    "georgian":"ka",
+    "belarusian":"be",
+    "tajik":"tg",
+    "sindhi":"sd",
+    "gujarati":"gu",
+    "amharic":"am",
+    "yiddish":"yi",
+    "lao":"lo",
+    "uzbek":"uz",
+    "faroese":"fo",
+    "haitian creole":"ht",
+    "pashto":"ps",
+    "turkmen":"tk",
+    "nynorsk":"nn",
+    "maltese":"mt",
+    "sanskrit":"sa",
+    "luxembourgish":"lb",
+    "myanmar":"my",
+    "tibetan":"bo",
+    "tagalog":"tl",
+    "malagasy":"mg",
+    "assamese":"as",
+    "tatar":"tt",
+    "hawaiian":"haw",
+    "lingala":"ln",
+    "hausa":"ha",
+    "bashkir":"ba",
+    "javanese":"jw",
+    "sundanese":"su",
+}
+
+def decode(model, mel, options):
+    result = whisper.decode(model, mel, options)
+    return result.text
+
+def load_audio(path):
+    waveform, sample_rate = torchaudio.load(path)
+    if sample_rate != 16000:
+        waveform = torchaudio.transforms.Resample(sample_rate, 16000)(waveform)
+    return waveform.squeeze(0)
+
+def detect_language(model, mel):
+    _, probs = model.detect_language(mel)
+    return max(probs, key=probs.get)
+
+def main():
+
+    st.title("Whisper ASR Demo")
+    st.markdown(
+            """
+        This is a demo of OpenAI's Whisper ASR model. The model is trained on 680,000 hours of dataset. 
+        """
+    )
+
+    model_selection = st.sidebar.selectbox("Select model", ["tiny", "base", "small", "medium", "large"])
+    en_model_selection = st.sidebar.checkbox("English only model", value=False)
+
+    if en_model_selection:
+        model_selection += ".en"
+    st.sidebar.write(f"Model: {model_selection+' (Multilingual)' if not en_model_selection else model_selection + ' (English only)'}")
+
+    if st.sidebar.checkbox("Show supported languages", value=False):
+            st.sidebar.info(list(LANGUAGES.keys()))
+    st.sidebar.title("Options")
+    
+    beam_size = st.sidebar.slider("Beam Size", min_value=1, max_value=10, value=5)
+    fp16 = st.sidebar.checkbox("Enable FP16 for faster transcription (It may affect performance)", value=False)
+
+    if not en_model_selection:
+        task = st.sidebar.selectbox("Select task", ["transcribe", "translate (To English)"], index=0)
+    else:
+        task = st.sidebar.selectbox("Select task", ["transcribe"], index=0)
+
+    st.title("Audio")
+    audio_file = st.file_uploader("Upload Audio", type=["wav", "mp3", "flac"])
+
+    if audio_file is not None:
+        st.audio(audio_file, format='audio/ogg')
+        with st.spinner("Loading model..."):
+            model = whisper.load_model(model_selection)
+            model = model.to("cpu") if not torch.cuda.is_available() else model.to("cuda")
+            
+
+        audio = load_audio(audio_file)
+        with st.spinner("Extracting features..."):
+            audio = whisper.pad_or_trim(audio)
+            mel = whisper.log_mel_spectrogram(audio).to(model.device)
+        if not en_model_selection:
+            with st.spinner("Detecting language..."):
+                language = detect_language(model, mel)
+                st.markdown(f"Detected Language: {language}")
+        else:
+            language = "en"
+        configuration = {"beam_size": beam_size, "fp16": fp16, "task": task, "language": language}
+        with st.spinner("Transcribing..."):
+            options = whisper.DecodingOptions(**configuration)
+            text = decode(model, mel, options)
+        st.markdown(f"**Recognized Text:** {text}")
+
+if __name__ == "__main__":
+    main()

packages.txt

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+ffmpeg

requirements.txt

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+numpy
+torch
+torchaudio
+tqdm
+more-itertools
+transformers>=4.19.0
+ffmpeg-python==0.2.0