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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}") |