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import gradio as gr |
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import vowel_length |
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import ctcalign |
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meta_tsv = ['data/set1.tsv','data/set2.tsv'] |
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ph_key = 'data/key_all.tsv' |
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align_output = 'data/align_csv.pickle' |
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dat,vck,kws,csvs = vowel_length.setup(meta_tsv,ph_key,align_output) |
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def manager(word,group,aligner,side): |
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fig = vowel_length.runan(word,group,aligner,vck,dat,csvs) |
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print(side) |
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return fig |
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def aligning(transcript, audio, language): |
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formatted_output = ctcalign.langsalign(audio,transcript,language) |
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return formatted_output |
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bl = gr.Blocks() |
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with bl: |
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with gr.Tabs(): |
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with gr.TabItem("Vowel quantity"): |
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gr.Markdown( |
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""" |
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# Long and short Icelandic vowels |
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Choose a word, speaker group, and aligner type. Available speaker groups are native speakers, second-language speakers, or all. Aligner options are Montreal Forced Aligner (MFA) and CTC decoding with Wav2vec-2.0. |
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The general expectation is that syllables with long stressed vowels followed by short consonants have a higher vowel:consonant duration ratio, while syllables with short stressed vowels followed by long consonants have a lower vowel:consonant ratio. However, a great many other factors affect the relative duration in any one recorded token. See Pind 1999, 'Speech segment durations and quantity in Icelandic' (J. Acoustical Society of America, 106(2)) for a review of the acoustics of Icelandic vowel duration. |
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All phoneme durations are measured automatically with no human correction. The purpose of this demo is to evaluate the role of such tools in large-scale phonetic research. Therefore, no measurements shown in this demo should be taken as conclusive without some independent verification. |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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wmenu1 = gr.Dropdown(kws,label="Word",value="hala") |
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lmenu1 = gr.Dropdown(["L1", "L2","All"],label="Speaker group",value="L1") |
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amenu1 = gr.Dropdown(["MFA", "CTC"],label="Aligner",value="CTC") |
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btn1 = gr.Button(value="Update Plot 1") |
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pl1 = gr.Plot() |
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btn1.click(manager, [wmenu1, lmenu1, amenu1, btn1], pl1) |
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with gr.Column(): |
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wmenu2 = gr.Dropdown(kws,label="Word",value="halla") |
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lmenu2 = gr.Dropdown(["L1", "L2","All"],label="Speaker group",value="L1") |
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amenu2 = gr.Dropdown(["MFA", "CTC"],label="Aligner",value="CTC") |
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btn2 = gr.Button(value="Update Plot 2") |
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pl2 = gr.Plot() |
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btn2.click(manager, [wmenu2, lmenu2, amenu2, btn2], pl2) |
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with gr.TabItem("CTC alignment"): |
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gr.Markdown( |
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""" |
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# Forced alignment with CTC decoding |
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Choose a language to upload a sentence with corresponding text. Generate word and letter time-alignments from the language's wav2vec-2.0 model, with output in MFA (Montreal Forced Aligner)-compatible format. It is best to upload short recordings of a sentence or so; recordings over a couple minutes require excessive memory to align, and should be divided into shorter pieces. |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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transcript_boxx = gr.Textbox(label="Transcript",placeholder="Type or paste the transcript here. Capitalisation and punctuation, if any, will be ignored.") |
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audio_file = gr.Audio(type="filepath") |
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alangmenu = gr.Radio(["Icelandic", "Faroese", "Norwegian"],value="Icelandic",label="Language") |
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al_btn = gr.Button(value="Run forced alignment") |
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with gr.Column(): |
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output_box = gr.Textbox(label="Forced alignment output") |
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al_btn.click(aligning, [transcript_boxx, audio_file, alangmenu], output_box) |
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if __name__ == "__main__": |
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bl.launch() |
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