app.py

import gradio as gr

from transformers import pipeline

DEFAULT_MODEL = "ginic/data_seed_bs64_4_wav2vec2-large-xlsr-53-buckeye-ipa"


VALID_MODELS = [
    "ctaguchi/wav2vec2-large-xlsr-japlmthufielta-ipa-plus-2000",
    "ginic/data_seed_bs64_1_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/data_seed_bs64_2_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/data_seed_bs64_3_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/data_seed_bs64_4_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_30_female_1_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_30_female_2_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_30_female_3_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_30_female_4_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_30_female_5_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_70_female_1_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_70_female_2_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_70_female_3_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_70_female_4_wav2vec2-large-xlsr-53-buckeye-ipa",
    "ginic/gender_split_70_female_5_wav2vec2-large-xlsr-53-buckeye-ipa",
    
]


def load_model_and_predict(model_name, audio_in, model_state):
    if model_state["model_name"] != model_name:
        model_state = {
            "loaded_model": pipeline(
                task="automatic-speech-recognition", model=model_name
            ),
            "model_name": model_name,
        }

    return model_state["loaded_model"](audio_in)["text"], model_state


def launch_demo():
    initial_model = {
        "loaded_model": pipeline(
            task="automatic-speech-recognition", model=DEFAULT_MODEL
        ),
        "model_name": DEFAULT_MODEL,
    }
    demo = gr.Interface(
        fn=load_model_and_predict,
        inputs=[
            gr.Dropdown(
                VALID_MODELS,
                value=DEFAULT_MODEL,
                label="IPA transcription ASR model",
                info="Select the model to use for prediction.",
            ),
            gr.Audio(type="filepath"),
            gr.State(
                value=initial_model
            ),  # Store the name of the currently loaded model
        ],
        outputs=[gr.Textbox(label="Predicted IPA transcription"), gr.State()],
        allow_flagging="never",
        title="Automatic International Phonetic Alphabet Transcription",
        description="This demo allows you to experiment with producing phonetic transcriptions of uploaded or recorded audio using a selected automatic speech recognition (ASR) model.",
    )

    demo.launch()


if __name__ == "__main__":
    launch_demo()