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import io
import whisper
import torch
import ffmpeg
import torchaudio
import streamlit as st
LANGUAGES = {
"en":"english",
"zh":"chinese",
"de":"german",
"es":"spanish",
"ru":"russian",
"ko":"korean",
"fr":"french",
"ja":"japanese",
"pt":"portuguese",
"tr":"turkish",
"pl":"polish",
"ca":"catalan",
"nl":"dutch",
"ar":"arabic",
"sv":"swedish",
"it":"italian",
"id":"indonesian",
"hi":"hindi",
"fi":"finnish",
"vi":"vietnamese",
"iw":"hebrew",
"uk":"ukrainian",
"el":"greek",
"ms":"malay",
"cs":"czech",
"ro":"romanian",
"da":"danish",
"hu":"hungarian",
"ta":"tamil",
"no":"norwegian",
"th":"thai",
"ur":"urdu",
"hr":"croatian",
"bg":"bulgarian",
"lt":"lithuanian",
"la":"latin",
"mi":"maori",
"ml":"malayalam",
"cy":"welsh",
"sk":"slovak",
"te":"telugu",
"fa":"persian",
"lv":"latvian",
"bn":"bengali",
"sr":"serbian",
"az":"azerbaijani",
"sl":"slovenian",
"kn":"kannada",
"et":"estonian",
"mk":"macedonian",
"br":"breton",
"eu":"basque",
"is":"icelandic",
"hy":"armenian",
"ne":"nepali",
"mn":"mongolian",
"bs":"bosnian",
"kk":"kazakh",
"sq":"albanian",
"sw":"swahili",
"gl":"galician",
"mr":"marathi",
"pa":"punjabi",
"si":"sinhala",
"km":"khmer",
"sn":"shona",
"yo":"yoruba",
"so":"somali",
"af":"afrikaans",
"oc":"occitan",
"ka":"georgian",
"be":"belarusian",
"tg":"tajik",
"sd":"sindhi",
"gu":"gujarati",
"am":"amharic",
"yi":"yiddish",
"lo":"lao",
"uz":"uzbek",
"fo":"faroese",
"ht":"haitian creole",
"ps":"pashto",
"tk":"turkmen",
"nn":"nynorsk",
"mt":"maltese",
"sa":"sanskrit",
"lb":"luxembourgish",
"my":"myanmar",
"bo":"tibetan",
"tl":"tagalog",
"mg":"malagasy",
"as":"assamese",
"tt":"tatar",
"haw":"hawaiian",
"ln":"lingala",
"ha":"hausa",
"ba":"bashkir",
"jw":"javanese",
"su":"sundanese",
}
def decode(model, mel, options):
result = whisper.decode(model, mel, options)
return result.text
def load_audio(audio):
print(audio.type)
if audio.type == "audio/wav" or audio.type == "audio/flac":
wave, sr = torchaudio.load(audio)
if sr != 16000:
wave = torchaudio.transforms.Resample(sr, 16000)(wave)
return wave.squeeze(0)
elif audio.type == "audio/mpeg":
audio = audio.read()
audio, _ = (ffmpeg
.input('pipe:0')
.output('pipe:1', format='wav', acodec='pcm_s16le', ac=1, ar='16k')
.run(capture_stdout=True, input=audio)
)
audio = io.BytesIO(audio)
wave, sr = torchaudio.load(audio)
if sr != 16000:
wave = torchaudio.transforms.Resample(sr, 16000)(wave)
return wave.squeeze(0)
else:
st.error("Unsupported audio format")
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.values()))
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_file.type)
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: {LANGUAGES[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() |