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"""
Description:
This script presents a Gradio demo for the ASR/ST FAMA models developed at FBK
Dependencies:
all the necessary dependencies are listed in requirements.txt
Usage:
The demo can be runned locally by installing all necessary dependencies in a python virtual env or it can be run in an HuggingFace Space
Author: Lorenzo Concina
Date: 4/6/2025
"""
import os
import torch
import librosa as lb
import gradio as gr
from transformers import AutoProcessor, pipeline
from datasets import load_dataset
def load_fama(model_id, output_lang):
processor = AutoProcessor.from_pretrained(model_id)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
tgt_lang = "it"
# Force the model to start with the language tag
lang_tag = "<lang:{}>".format(output_lang)
lang_tag_id = processor.tokenizer.convert_tokens_to_ids(lang_tag)
generate_kwargs = {"num_beams": 5, "no_repeat_ngram_size": 5, "forced_bos_token_id": lang_tag_id}
pipe = pipeline(
"automatic-speech-recognition",
model=model_id,
trust_remote_code=True,
torch_dtype=torch.float32,
device=device,
return_timestamps=False,
generate_kwargs=generate_kwargs
)
return pipe
def load_audio_file(audio_path):
y, sr = lb.load(audio_path, sr=16000, mono=True)
return y
def transcribe(audio, task_type, model_id, output_lang):
"""
Function called by gradio interface. It runs model inference on an audio sample
"""
cache_key = (model_id, output_lang)
if cache_key not in model_cache:
model_cache[cache_key] = load_fama(model_id, output_lang)
pipeline = model_cache[cache_key]
if isinstance(audio, str) and os.path.isfile(audio):
#load the audio with Librosa
utterance = load_audio_file(audio)
result = pipeline(utterance)
else:
#user used the mic
result = pipeline(audio)
return result["text"]
#available models
def update_model_options(task_type):
if task_type == "ST":
return gr.update(choices=["FBK-MT/fama-small", "FBK-MT/fama-medium"], value="FBK-MT/fama-small")
else:
return gr.update(choices=[
"FBK-MT/fama-small",
"FBK-MT/fama-medium",
"FBK-MT/fama-small-asr",
"FBK-MT/fama-medium-asr"
], value="FBK-MT/fama-small")
# Language options (languages supported by FAMA models)
language_choices = ["en", "it"]
# Cache loaded models to avoid reloading
model_cache = {}
if __name__ == "__main__":
with gr.Blocks() as iface:
gr.Markdown("""## FAMA ASR and ST\nSimple Automatic Speech Recognition and Speech Translation demo powered by FAMA models, developed at FBK. \
More informations about FAMA models can be found here: https://huggingface.co/collections/FBK-MT/fama-683425df3fb2b3171e0cdc9e""")
with gr.Row():
audio_input = gr.Audio(type="filepath", label="Upload or record audio")
task_type_input = gr.Radio(choices=["ASR", "ST"], value="ASR", label="Select task type")
model_input = gr.Radio(choices=[
"FBK-MT/fama-small",
"FBK-MT/fama-medium",
"FBK-MT/fama-small-asr",
"FBK-MT/fama-medium-asr"
], value="FBK-MT/fama-small", label="Select a FAMA model")
lang_input = gr.Dropdown(choices=language_choices, value="it", label="Transcription language")
output = gr.Textbox(label="Transcription")
task_type_input.change(fn=update_model_options, inputs=task_type_input, outputs=model_input)
transcribe_btn = gr.Button("Transcribe")
transcribe_btn.click(fn=transcribe, inputs=[audio_input, task_type_input, model_input, lang_input], outputs=output)
iface.launch()
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