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import streamlit as st
from PIL import Image
import torch
import whisper
import joblib
import os
import json
import pydub
#
st.set_page_config(page_title="Detect the language of the audio file App", layout="centered")
st.image("image.jpg", caption='Detect the language of the audio file')
# page header
#st.title(f"English Text to Hindi Translation App")
with st.form("Prediction_form"):
uploadFile = st.file_uploader(label = "Please upload your file ",type=["mp3"])
if uploadFile is not None:
audio = pydub.AudioSegment.from_mp3(uploadFile)
file_type = 'mp3'
#filename = uploadFile.name+".mp3"
audio.export(uploadFile.name, format="mp3")
audio1 = open(uploadFile.name, "rb")
audio_bytes = audio1.read() #reading the file
st.audio(audio_bytes, format='audio/ogg') #displaying the audio
#
submit = st.form_submit_button("Detect Language")
#
if submit:
model = whisper.load_model("large").to('cpu')
# load audio and pad/trim it to fit 30 seconds
audio = whisper.load_audio(uploadFile.name)
audio = whisper.pad_or_trim(audio)
# make log-Mel spectrogram and move to the same device as the model
mel = whisper.log_mel_spectrogram(audio).to(model.device)
# detect the spoken language
_, probs = model.detect_language(mel)
# decode the audio
options = whisper.DecodingOptions(fp16 = False)
result = whisper.decode(model, mel, options)
languages = {"af_za": "Afrikaans", "am_et": "Amharic",
"ar_eg": "Arabic", "as_in": "Assamese",
"az_az": "Azerbaijani", "be_by": "Belarusian",
"bg_bg": "Bulgarian", "bn_in": "Bengali",
"bs_ba": "Bosnian", "ca_es": "Catalan",
"cmn_hans_cn": "Chinese", "cs_cz": "Czech",
"cy_gb": "Welsh", "da_dk": "Danish",
"de_de": "German", "el_gr": "Greek",
"en_us": "English", "es_419": "Spanish",
"et_ee": "Estonian", "fa_ir": "Persian",
"fi_fi": "Finnish", "fil_ph": "Tagalog",
"fr_fr": "French", "gl_es": "Galician",
"gu_in": "Gujarati", "ha_ng": "Hausa",
"he_il": "Hebrew", "hi_in": "Hindi",
"hr_hr": "Croatian", "hu_hu": "Hungarian",
"hy_am": "Armenian", "id_id": "Indonesian",
"is_is": "Icelandic", "it_it": "Italian",
"ja_jp": "Japanese", "jv_id": "Javanese",
"ka_ge": "Georgian", "kk_kz": "Kazakh",
"km_kh": "Khmer", "kn_in": "Kannada",
"ko_kr": "Korean", "lb_lu": "Luxembourgish",
"ln_cd": "Lingala", "lo_la": "Lao",
"lt_lt": "Lithuanian", "lv_lv": "Latvian",
"mi_nz": "Maori", "mk_mk": "Macedonian",
"ml_in": "Malayalam", "mn_mn": "Mongolian",
"mr_in": "Marathi", "ms_my": "Malay",
"mt_mt": "Maltese", "my_mm": "Myanmar",
"nb_no": "Norwegian", "ne_np": "Nepali",
"nl_nl": "Dutch", "oc_fr": "Occitan",
"pa_in": "Punjabi", "pl_pl": "Polish",
"ps_af": "Pashto", "pt_br": "Portuguese",
"ro_ro": "Romanian", "ru_ru": "Russian",
"sd_in": "Sindhi", "sk_sk": "Slovak",
"sl_si": "Slovenian", "sn_zw": "Shona", "so_so": "Somali", "sr_rs": "Serbian", "sv_se": "Swedish", "sw_ke": "Swahili", "ta_in": "Tamil", "te_in": "Telugu", "tg_tj": "Tajik", "th_th": "Thai", "tr_tr": "Turkish", "uk_ua": "Ukrainian",
"ur_pk": "Urdu", "uz_uz": "Uzbek", "vi_vn": "Vietnamese", "yo_ng": "Yoruba"}
decode_language = {k.split("_")[0]: v for k,v in languages.items()}
language = decode_language[result.language]
text = result.text
out = f"Language Detected: {language} Text: {text}"
# output header
st.header("Summary Gnereated")
# output results
st.success(f"Translated Text : {out}") |