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Running
Add "about" page and examples from CO.
Browse files
app.py
CHANGED
@@ -2,6 +2,7 @@ import torch
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import scipy
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import os
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import streamlit as st
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from transformers import set_seed, pipeline
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from transformers import VitsTokenizer, VitsModel
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from datasets import load_dataset, Audio
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@@ -20,7 +21,7 @@ language_list = ['mos', 'fra', 'eng']
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st.title("Demo: Automated Tools for Mooré Language")
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tts, stt, trans, lid = st.tabs(["Text to speech", "Speech to text", "Translation", "Language ID"])
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########################
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with tts:
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@@ -40,6 +41,7 @@ with tts:
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synth = synthesize_facebook(tts_text, tts_lang)
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st.audio(synth, sample_rate=16_000)
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########################
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with stt:
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@@ -54,6 +56,16 @@ with stt:
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":violet[The transcription is:]"
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':violet[ "' + stt + '"]'
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########################
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with trans:
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translation = translate(trans_text, src_lang, target_lang) #, trans_model)
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translation
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########################
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with lid:
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langid_file = st.file_uploader("Please upload an audio file:", type=['mp3', 'm4a'], key = "lid_uploader")
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lang = decode_iso(lang)
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":violet[The detected language is " + lang + "]"
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import scipy
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import os
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import streamlit as st
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import pandas as pd
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from transformers import set_seed, pipeline
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from transformers import VitsTokenizer, VitsModel
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from datasets import load_dataset, Audio
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st.title("Demo: Automated Tools for Mooré Language")
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tts, stt, trans, lid, about = st.tabs(["Text to speech", "Speech to text", "Translation", "Language ID", "**About**"])
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########################
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with tts:
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synth = synthesize_facebook(tts_text, tts_lang)
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st.audio(synth, sample_rate=16_000)
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########################
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with stt:
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":violet[The transcription is:]"
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':violet[ "' + stt + '"]'
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st.subheader("Examples")
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"Using the supplied clips, here are the transcriptions:"
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df = pd.read_csv("data/speech_to_text.csv")
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df.columns = ['Clip ID', 'Spoken in Moore', 'Spoken in French', 'Transcription in Moore', 'Transcription in French']
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df.set_index('Clip ID', inplace=True)
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st.table(df[['Spoken in Moore', 'Transcription in Moore']])
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st.table(df[['Spoken in French', 'Transcription in French']])
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########################
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with trans:
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translation = translate(trans_text, src_lang, target_lang) #, trans_model)
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translation
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st.subheader("Examples")
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"Using the supplied clips, here are the translations:"
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df = pd.read_csv("data/translated_eng.csv",
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usecols=['ID', 'French', 'Moore', 'English',
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'tr_meta_mos_fra', 'tr_meta_mos_eng', 'tr_meta_eng_mos', 'tr_meta_fra_mos'])
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df.columns = ['Clip ID', 'Original Moore', 'Original French', 'Original English',
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'Moore-English Translation', 'Moore-French Translation',
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'English-Moore Translation', 'French-Moore Translation']
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df.set_index('Clip ID', inplace=True)
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st.table(df[['Original Moore', 'Moore-French Translation', 'Moore-English Translation']])
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st.table(df[['Original French', 'French-Moore Translation']])
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st.table(df[['Original English', 'English-Moore Translation']])
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########################
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with lid:
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langid_file = st.file_uploader("Please upload an audio file:", type=['mp3', 'm4a'], key = "lid_uploader")
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lang = decode_iso(lang)
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":violet[The detected language is " + lang + "]"
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st.subheader("Examples")
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"Using the supplied clips, here are the recognized languages:"
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df = pd.read_csv("data/language_id.csv")
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df.columns = ['Clip ID', 'Language detected when speaking Mooré', 'Language detected when speaking French']
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df.set_index('Clip ID', inplace=True)
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st.dataframe(df)
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# supported colors: blue, green, orange, red, violet, gray/grey, rainbow.
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# https://docs.streamlit.io/library/api-reference/text/st.markdown
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with about:
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#st.header("How it works")
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st.markdown('''
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**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][^2]
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**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]
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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]
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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.
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[^1]: Endpoints used: TTS ([English](https://huggingface.co/facebook/mms-tts-eng),
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[French](https://huggingface.co/facebook/mms-tts-fra),
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[Mooré](https://huggingface.co/facebook/mms-tts-mos)),
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[STT](https://huggingface.co/facebook/mms-1b-all),
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[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.
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Learn more:
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[Docs](https://huggingface.co/docs/transformers/model_doc/mms) |
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[Paper](https://arxiv.org/abs/2305.13516) |
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[Supported languages](https://dl.fbaipublicfiles.com/mms/misc/language_coverage_mms.html)
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[^3]: Endpoint used: [NLLB](https://huggingface.co/facebook/nllb-200-distilled-600M).
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Learn more:
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[Docs](https://huggingface.co/docs/transformers/model_doc/nllb) |
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[Paper](https://huggingface.co/docs/transformers/model_doc/nllb) |
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[Supported languages](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
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[^4]: Endpoint used: [Mooré to French](https://huggingface.co/masakhane/m2m100_418M_mos_fr_news),
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[French to Mooré](https://huggingface.co/masakhane/m2m100_418M_fr_mos_news).
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Learn more:
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[Docs](https://github.com/masakhane-io/lafand-mt) |
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[Paper](https://arxiv.org/abs/2205.02022)
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[^5]: Endpoints used: [Mooré to English](https://huggingface.co/Helsinki-NLP/opus-mt-mos-en),
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[English to Mooré](https://huggingface.co/Helsinki-NLP/opus-mt-en-mos),
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[French to Mooré](https://huggingface.co/Helsinki-NLP/opus-mt-fr-mos).
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Learn more:
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[Docs](https://github.com/Helsinki-NLP/Opus-MT)
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''')
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