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) ''')