Updated

app.py

@@ -1,39 +1,39 @@
-import gradio
-import torch
-import numpy as np
-from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForTextToWaveform
-
-# Load your pretrained models
-asr_model = Wav2Vec2ForCTC.from_pretrained("Baghdad99/saad-speech-recognition-hausa-audio-to-text")
-asr_processor = Wav2Vec2Processor.from_pretrained("Baghdad99/saad-speech-recognition-hausa-audio-to-text")
-translation_tokenizer = AutoTokenizer.from_pretrained("Baghdad99/saad-hausa-text-to-english-text")
-translation_model = AutoModelForSeq2SeqLM.from_pretrained("Baghdad99/saad-hausa-text-to-english-text", from_tf=True)
-tts_tokenizer = AutoTokenizer.from_pretrained("Baghdad99/english_voice_tts")
-tts_model = AutoModelForTextToWaveform.from_pretrained("Baghdad99/english_voice_tts")
-
-# Modify the translate function to accept the sampling_rate argument
-def translate(audio_signal, sampling_rate):
-    inputs = asr_processor(audio_signal, sampling_rate=sampling_rate, return_tensors="pt", padding=True)
-    logits = asr_model(inputs.input_values).logits
-    predicted_ids = torch.argmax(logits, dim=-1)
-    transcription = asr_processor.decode(predicted_ids[0])
-    translated = translation_model.generate(**translation_tokenizer(transcription, return_tensors="pt", padding=True))
-    translated_text = [translation_tokenizer.decode(t, skip_special_tokens=True) for t in translated]
-    return translated_text
-
-def synthesise(translated_text):
-    inputs = tts_tokenizer(translated_text, return_tensors='pt')
-    audio = tts_model.generate(inputs['input_ids'])
-    return audio
-
-def translate_speech(audio, sampling_rate):
-    translated_text = translate(audio, sampling_rate=sampling_rate)
-    synthesised_speech = synthesise(translated_text)
+import gradio as gr
+from transformers import pipeline
+
+# Load the pipeline for speech recognition and translation
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model="Baghdad99/saad-speech-recognition-hausa-audio-to-text",
+    tokenizer="Baghdad99/saad-speech-recognition-hausa-audio-to-text"
+)
+translator = pipeline("text2text-generation", model="Baghdad99/saad-hausa-text-to-english-text")
+tts = pipeline("text-to-speech", model="Baghdad99/english_voice_tts")
+
+# Define the function to translate speech
+def translate_speech(audio):
+    # Use the speech recognition pipeline to transcribe the audio
+    transcription = pipe(audio, sampling_rate=16000)[0]["transcription"]
+
+    # Use the translation pipeline to translate the transcription
+    translated_text = translator(transcription, return_tensors="pt", padding=True)
+
+    # Use the text-to-speech pipeline to synthesize the translated text
+    synthesised_speech = tts(translated_text, return_tensors='pt')
+
     # Define the max_range variable
     max_range = 32767  # You can adjust this value based on your requirements
     synthesised_speech = (synthesised_speech.numpy() * max_range).astype(np.int16)
+
     return 16000, synthesised_speech
 
 # Define the Gradio interface
-iface = gradio.Interface(fn=translate_speech, inputs=gradio.inputs.Audio(source="microphone", type="numpy"), outputs="audio")
+iface = gr.Interface(
+    fn=translate_speech, 
+    inputs=gr.inputs.Audio(source="microphone", type="numpy"), 
+    outputs=gr.outputs.Audio(type="numpy"),
+    title="Hausa to English Translation",
+    description="Realtime demo for Hausa to English translation using speech recognition and text-to-speech synthesis."
+)
+
 iface.launch()