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import gradio as gr
from huggingface_hub import InferenceClient
import soundfile as sf
import torch
from transformers import pipeline
# Set up your TTS model (as before)
synthesiser = pipeline("text-to-speech", "Futuresony/output")
# Set up your text generation client
client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
# Generate text response from your model
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
# Convert the generated text into speech (Text-to-Speech)
# Get speaker embedding (optional, if you want to control the speaker)
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
# Generate speech from the text response
speech = synthesiser(response, forward_params={"speaker_embeddings": speaker_embedding})
# Save the speech to a file (you can play it on the fly or return it in other formats like MP3)
sf.write("generated_speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
return response, "generated_speech.wav"
# You can return the text along with speech if needed
# Create the Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
],
)
if __name__ == "__main__":
demo.launch()
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