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| import torch | |
| import scipy | |
| import os | |
| import streamlit as st | |
| from transformers import set_seed, pipeline | |
| from transformers import VitsTokenizer, VitsModel | |
| from datasets import load_dataset, Audio | |
| from src import * | |
| #from huggingface_hub import login | |
| #from dotenv import load_dotenv | |
| #load_dotenv() | |
| #HUGGINGFACE_KEY = os.environ.get("HUGGINGFACE_KEY") | |
| #login(HUGGINGFACE_KEY) | |
| ######################## | |
| language_list = ['mos', 'fra', 'eng'] | |
| st.title("Demo: Automated Tools for Mooré Language") | |
| tts, stt, trans, lid = st.tabs(["Text to speech", "Speech to text", "Translation", "Language ID"]) | |
| ######################## | |
| with tts: | |
| tts_text = st.text_area(label = "Please enter your text here:", value="", placeholder="ne y wĩndga") | |
| tts_col1, tts_col2, = st.columns(2) | |
| with tts_col1: | |
| tts_lang = st.selectbox('Language of text', (language_list), format_func = decode_iso) | |
| if st.button("Speak"): | |
| st.divider() | |
| with st.spinner(":rainbow[Synthesizing, please wait...]"): | |
| synth = synthesize_facebook(tts_text, tts_lang) | |
| st.audio(synth, sample_rate=16_000) | |
| ######################## | |
| with stt: | |
| stt_file = st.file_uploader("Please upload an audio file:", type=['mp3', 'm4a'], key = "stt_uploader") | |
| stt_lang = st.selectbox("Please select the language:" , (language_list), format_func = decode_iso) | |
| if st.button("Transcribe"): | |
| st.divider() | |
| with st.spinner(":rainbow[Received your file, please wait while I process it...]"): | |
| stt = transcribe(stt_file, stt_lang) | |
| ":violet[The transcription is:]" | |
| ':violet[ "' + stt + '"]' | |
| ######################## | |
| with trans: | |
| trans_text = st.text_area(label = "Please enter your translation text here:", value="", placeholder="ne y wĩndga") | |
| #trans_col1, trans_col2, trans_col3 = st.columns([.25, .25, .5]) | |
| trans_col1, trans_col2 = st.columns(2) | |
| with trans_col1: | |
| src_lang = st.selectbox('Translate from:', (language_list), format_func = decode_iso) | |
| with trans_col2: | |
| target_lang = st.selectbox('Translate to:', (language_list), format_func = decode_iso, index=1) | |
| #with trans_col3: | |
| # trans_model = st.selectbox("Translation model:", | |
| # ("Facebook (nllb-200-distilled-600M)", | |
| # "Helsinki NLP (opus-mt-mos-en)", | |
| # "Masakhane (m2m100_418m_mos_fr_news)") | |
| # ) | |
| if st.button("Translate"): | |
| st.divider() | |
| with st.spinner(":rainbow[Translating from " + decode_iso(src_lang) + " into " + decode_iso(target_lang) + ", please wait...]"): | |
| translation = translate(trans_text, src_lang, target_lang) #, trans_model) | |
| translation | |
| ######################## | |
| with lid: | |
| langid_file = st.file_uploader("Please upload an audio file:", type=['mp3', 'm4a'], key = "lid_uploader") | |
| if st.button("Identify"): | |
| st.divider() | |
| with st.spinner(":rainbow[Received your file, please wait while I process it...]"): | |
| lang = identify_language(langid_file) | |
| lang = decode_iso(lang) | |
| ":violet[The detected language is " + lang + "]" | |
| # supported colors: blue, green, orange, red, violet, gray/grey, rainbow. | |
| # https://docs.streamlit.io/library/api-reference/text/st.markdown | |