app.py

import torch
import scipy
import os
import streamlit as st
import pandas as pd
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, about = st.tabs(["Text to speech", "Speech to text", "Translation", "Language ID", "**About**"])

########################
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 + '"]'

    st.subheader("Examples")
    "Using the supplied clips, here are the transcriptions:"
    df = pd.read_csv("data/speech_to_text.csv")
    df.columns = ['Clip ID', 'Spoken in Moore', 'Spoken in French', 'Transcription in Moore', 'Transcription in French']
    
    df.set_index('Clip ID', inplace=True)
    st.table(df[['Spoken in Moore', 'Transcription in Moore']])
    
    st.table(df[['Spoken in French', 'Transcription in French']])

########################
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



    st.subheader("Examples")
    "Using the supplied clips, here are the translations:"
    df = pd.read_csv("data/translated_eng.csv",
                    usecols=['ID', 'French', 'Moore', 'English', 
                             'tr_meta_mos_fra', 'tr_meta_mos_eng', 'tr_meta_eng_mos', 'tr_meta_fra_mos'])
    
    df.columns = ['Clip ID',  'Original Moore', 'Original French', 'Original English',
                         'Moore-English Translation', 'Moore-French Translation', 
                     'English-Moore Translation', 'French-Moore Translation']
    
    df.set_index('Clip ID', inplace=True)
    
    st.table(df[['Original Moore', 'Moore-French Translation', 'Moore-English Translation']])
    st.table(df[['Original French', 'French-Moore Translation']])
    st.table(df[['Original English', 'English-Moore 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 + "]"

    st.subheader("Examples")
    "Using the supplied clips, here are the recognized languages:"
    df = pd.read_csv("data/language_id.csv")
    df.columns = ['Clip ID', 'Language detected when speaking Mooré', 'Language detected when speaking French']
    df.set_index('Clip ID', inplace=True)
    st.dataframe(df)


    # supported colors: blue, green, orange, red, violet, gray/grey, rainbow.
    # https://docs.streamlit.io/library/api-reference/text/st.markdown

with about:
    #st.header("How it works")
    st.markdown('''
**Text to speech**, **speech to text**, and **language identification** capabilities are provided by Meta's [Massively Multilingual Speech (MMS)](https://ai.meta.com/blog/multilingual-model-speech-recognition/) model, which supports over 1000 languages.[^1]

**Translation** capabilities are provided primarily by Meta's [No Language Left Behind (NLLB)](https://ai.meta.com/research/no-language-left-behind/) model, which supports translation between 200 languages.[^3]
We compare Meta's NLLB translations to two other translation alternatives. Masakhane, an African NLP initiative, offers endpoints for translations between Mooré and French.[^4] Helsinki NLP offers enpoints between Mooré and English, and one endpoint from French to Mooré.[^5]

Facebook has since released [SeamlessM4T](https://huggingface.co/docs/transformers/main/model_doc/seamless_m4t) which also provides support for audio-to-audio translation, however, Mooré is not currently one of the included languages.
[^1]: Endpoints used: TTS ([English](https://huggingface.co/facebook/mms-tts-eng), 
    [French](https://huggingface.co/facebook/mms-tts-fra), 
    [Mooré](https://huggingface.co/facebook/mms-tts-mos)),
    [STT](https://huggingface.co/facebook/mms-1b-all), 
    [LID](https://huggingface.co/facebook/mms-lid-256).  For language ID, the 256-language variant was chosen as this was the model with the smallest number of languages, which still included Mooré.   
    Learn more:
    [Docs](https://huggingface.co/docs/transformers/model_doc/mms) | 
    [Paper](https://arxiv.org/abs/2305.13516) | 
    [Supported languages](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html)
[^3]: Endpoint used: [NLLB](https://huggingface.co/facebook/nllb-200-distilled-600M).   
    Learn more: 
    [Docs](https://huggingface.co/docs/transformers/model_doc/nllb) | 
    [Paper](https://huggingface.co/docs/transformers/model_doc/nllb) | 
    [Supported languages](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
[^4]: Endpoint used: [Mooré to French](https://huggingface.co/masakhane/m2m100_418M_mos_fr_news), 
    [French to Mooré](https://huggingface.co/masakhane/m2m100_418M_fr_mos_news).   
    Learn more:
    [Docs](https://github.com/masakhane-io/lafand-mt) |
    [Paper](https://arxiv.org/abs/2205.02022)
[^5]: Endpoints used: [Mooré to English](https://huggingface.co/Helsinki-NLP/opus-mt-mos-en),
    [English to Mooré](https://huggingface.co/Helsinki-NLP/opus-mt-en-mos),
    [French to Mooré](https://huggingface.co/Helsinki-NLP/opus-mt-fr-mos).   
    Learn more:
    [Docs](https://github.com/Helsinki-NLP/Opus-MT) 
''')