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import gradio as gr
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from huggingface_hub import InferenceClient
from ttsmms import download, TTS
from langdetect import detect
# Load ASR Model
asr_model_name = "Futuresony/Future-sw_ASR-24-02-2025"
processor = Wav2Vec2Processor.from_pretrained(asr_model_name)
asr_model = Wav2Vec2ForCTC.from_pretrained(asr_model_name)
# Load Text Generation Model
client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
def format_prompt(user_input):
return f"{user_input}"
# Load TTS Models
swahili_dir = download("swh", "./data/swahili")
english_dir = download("eng", "./data/english")
swahili_tts = TTS(swahili_dir)
english_tts = TTS(english_dir)
# ASR Function
def transcribe(audio_file):
speech_array, sample_rate = torchaudio.load(audio_file)
resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)
speech_array = resampler(speech_array).squeeze().numpy()
input_values = processor(speech_array, sampling_rate=16000, return_tensors="pt").input_values
with torch.no_grad():
logits = asr_model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
return transcription
# Text Generation Function
def generate_text(prompt):
formatted_prompt = format_prompt(prompt)
response = client.text_generation(formatted_prompt, max_new_tokens=250, temperature=0.7, top_p=0.95)
return response.strip()
# TTS Function
def text_to_speech(text):
lang = detect(text)
wav_path = "./output.wav"
if lang == "sw":
swahili_tts.synthesis(text, wav_path=wav_path)
else:
english_tts.synthesis(text, wav_path=wav_path)
return wav_path
# Combined Processing Function
def process_audio(audio):
transcription = transcribe(audio)
generated_text = generate_text(transcription)
speech = text_to_speech(generated_text)
return transcription, generated_text, speech
# Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("<p align='center' style='font-size: 20px;'>End-to-End ASR, Text Generation, and TTS</p>")
gr.HTML("<center>Upload or record audio. The model will transcribe, generate a response, and read it out.</center>")
audio_input = gr.Audio(label="Input Audio", type="filepath")
text_output = gr.Textbox(label="Transcription")
generated_text_output = gr.Textbox(label="Generated Text")
audio_output = gr.Audio(label="Output Speech")
submit_btn = gr.Button("Submit")
submit_btn.click(
fn=process_audio,
inputs=audio_input,
outputs=[text_output, generated_text_output, audio_output]
)
if __name__ == "__main__":
demo.launch()
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