Update app.py

app.py

@@ -1,7 +1,119 @@
 import gradio as gr
+from transformers import AutoTokenizer, VitsModel, Wav2Vec2Processor, Wav2Vec2ForCTC
+import torch
+import scipy.io.wavfile
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
+# TTS Models
+twi_model_id = "FarmerlineML/main_twi_TTS_V2"
+twi_model = VitsModel.from_pretrained(twi_model_id)
+twi_tokenizer = AutoTokenizer.from_pretrained(twi_model_id)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+ewe_model_id = "FarmerlineML/main_ewe_TTS"
+ewe_model = VitsModel.from_pretrained(ewe_model_id)
+ewe_tokenizer = AutoTokenizer.from_pretrained(ewe_model_id)
+
+# ASR Models
+asr_models = {
+    "Twi": "FarmerlineML/akan_ASR_alpha",
+    "Ewe": "FarmerlineML/ewe_ASR_3.0"
+}
+
+# Initialize ASR models and processors
+asr_processors = {}
+asr_models_loaded = {}
+for lang, model_name in asr_models.items():
+    asr_processors[lang] = Wav2Vec2Processor.from_pretrained(model_name)
+    asr_models_loaded[lang] = Wav2Vec2ForCTC.from_pretrained(model_name)
+
+def generate_speech(text, language, noise_scale=0.8, noise_scale_duration=0.9, speaking_rate=1.0):
+    if language == "Twi":
+        model = twi_model
+        tokenizer = twi_tokenizer
+    else:  # Ewe
+        model = ewe_model
+        tokenizer = ewe_tokenizer
+
+    inputs = tokenizer(text, return_tensors="pt")
+    model.noise_scale = noise_scale
+    model.noise_scale_duration = noise_scale_duration
+    model.speaking_rate = speaking_rate
+    with torch.no_grad():
+        output = model(**inputs)
+    return output.waveform[0].cpu().numpy(), model.config.sampling_rate
+
+def tts_interface(text, language):
+    if not text.strip():
+        return None, "Please enter some text."
+
+    audio, sampling_rate = generate_speech(text, language)
+
+    # Save the audio to a file
+    output_file = f"output_{language.lower()}.wav"
+    scipy.io.wavfile.write(output_file, rate=sampling_rate, data=audio)
+
+    return output_file, f"{language} audio generated successfully."
+
+def transcribe_audio(audio, language):
+    processor = asr_processors[language]
+    model = asr_models_loaded[language]
+
+    # Preprocess the audio
+    input_values = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt", padding="longest").input_values
+
+    # Perform inference
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+    # Decode the output
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = processor.batch_decode(predicted_ids)
+
+    return transcription[0]
+
+def asr_interface(audio, language):
+    if audio is None:
+        return "Please upload or record audio."
+    return transcribe_audio(audio, language)
+
+def update_language(language):
+    return f"Selected language: {language}", f"Enter {language} text to convert to speech", f"Upload or record {language} audio"
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Twi and Ewe ASR and TTS Demo")
+
+    language_selector = gr.Dropdown(choices=["Twi", "Ewe"], label="Language", value="Twi")
+    language_display = gr.Textbox(label="Current Language", value="Selected language: Twi")
+
+    with gr.Tab("Text-to-Speech"):
+        tts_input = gr.Textbox(label="Enter Twi text to convert to speech")
+        tts_button = gr.Button("Generate Speech")
+        tts_output = gr.Audio(label="Generated Speech")
+        tts_message = gr.Textbox(label="Message")
+
+    with gr.Tab("Automatic Speech Recognition"):
+        asr_input = gr.Audio(label="Upload or record Twi audio")
+        asr_button = gr.Button("Transcribe")
+        asr_output = gr.Textbox(label="Transcription")
+
+    # Set up event handlers
+    language_selector.change(
+        update_language,
+        inputs=[language_selector],
+        outputs=[language_display, tts_input, asr_input]
+    )
+
+    tts_button.click(
+        tts_interface,
+        inputs=[tts_input, language_selector],
+        outputs=[tts_output, tts_message]
+    )
+
+    asr_button.click(
+        asr_interface,
+        inputs=[asr_input, language_selector],
+        outputs=[asr_output]
+    )
+
+# For Hugging Face Spaces, we use `demo.launch()` without any arguments
+demo.launch()