Create Chatty_Ashe.py

Chatty_Ashe.py

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+import gradio as gr
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, GPT2LMHeadModel, GPT2Tokenizer, VitsProcessor, VitsForConditionalGeneration
+
+# Load the ASR model and processor
+asr_processor = Wav2Vec2Processor.from_pretrained("/path/to/canary/processor")
+asr_model = Wav2Vec2ForCTC.from_pretrained("/path/to/canary/model")
+
+# Load the text processing model and tokenizer
+proc_tokenizer = GPT2Tokenizer.from_pretrained("/path/to/phi3/tokenizer")
+proc_model = GPT2LMHeadModel.from_pretrained("/path/to/phi3/model")
+
+# Load the TTS model and processor
+tts_processor = VitsProcessor.from_pretrained("facebook/vits-base")
+tts_model = VitsForConditionalGeneration.from_pretrained("facebook/vits-base")
+
+def process_speech(speech):
+    # Convert the speech to text
+    inputs = asr_processor(speech, sampling_rate=16_000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = asr_model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    transcription = asr_processor.decode(predicted_ids[0])
+
+    # Process the text
+    inputs = proc_tokenizer.encode(transcription + proc_tokenizer.eos_token, return_tensors='pt')
+    outputs = proc_model.generate(inputs, max_length=100, temperature=0.7, pad_token_id=proc_tokenizer.eos_token_id)
+    processed_text = proc_tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Convert the processed text to speech
+    inputs = tts_processor(processed_text, return_tensors="pt")
+    with torch.no_grad():
+        logits = tts_model(inputs["input_ids"]).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    audio = tts_processor.decode(predicted_ids)
+
+    return audio
+
+iface = gr.Interface(fn=process_speech, inputs=gr.inputs.Audio(source="microphone"), outputs="audio")
+
+iface.launch()