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import gradio as gr
import os
from huggingface_hub import InferenceClient
client = InferenceClient(token=os.getenv("HF_TOKEN"))
def generate_response(audio):
gr.Info("Transcribing Audio", duration=5)
question = client.automatic_speech_recognition(audio).text
messages = [{"role": "system", "content": ("You are a magic 8 ball."
"Someone will present to you a situation or question and your job "
"is to answer with a cryptic adage or proverb such as "
"'curiosity killed the cat' or 'The early bird gets the worm'."
"Keep your answers short and do not include the phrase 'Magic 8 Ball' in your response. If the question does not make sense or is off-topic, say 'Foolish questions get foolish answers.'"
"For example, 'Magic 8 Ball, should I get a dog?', 'A dog is ready for you but are you ready for the dog?'")},
{"role": "user", "content": f"Magic 8 Ball please answer this question - {question}"}]
response = client.chat_completion(messages, max_tokens=64, seed=random.randint(1, 5000),
model="mistralai/Mistral-7B-Instruct-v0.3")
response = response.choices[0].message.content.replace("Magic 8 Ball", "").replace(":", "")
return response, None, None
from streamer import ParlerTTSStreamer
from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
import numpy as np
import spaces
import torch
from threading import Thread
device = "cuda:0" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
torch_dtype = torch.float16 if device != "cpu" else torch.float32
repo_id = "parler-tts/parler_tts_mini_v0.1"
jenny_repo_id = "ylacombe/parler-tts-mini-jenny-30H"
model = ParlerTTSForConditionalGeneration.from_pretrained(
jenny_repo_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True
).to(device)
tokenizer = AutoTokenizer.from_pretrained(repo_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)
sampling_rate = model.audio_encoder.config.sampling_rate
frame_rate = model.audio_encoder.config.frame_rate
@spaces.GPU
def read_response(answer):
play_steps_in_s = 2.0
play_steps = int(frame_rate * play_steps_in_s)
description = "Jenny speaks at an average pace with a calm delivery in a very confined sounding environment with clear audio quality."
description_tokens = tokenizer(description, return_tensors="pt").to(device)
streamer = ParlerTTSStreamer(model, device=device, play_steps=play_steps)
prompt = tokenizer(answer, return_tensors="pt").to(device)
generation_kwargs = dict(
input_ids=description_tokens.input_ids,
prompt_input_ids=prompt.input_ids,
streamer=streamer,
do_sample=True,
temperature=1.0,
min_new_tokens=10,
)
set_seed(42)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
for new_audio in streamer:
print(f"Sample of length: {round(new_audio.shape[0] / sampling_rate, 2)} seconds")
yield answer, numpy_to_mp3(new_audio, sampling_rate=sampling_rate)
with gr.Blocks() as block:
gr.HTML(
f"""
<h1 style='text-align: center;'> Hey Annie</h1>
<h3 style='text-align: center;'> Ask a question and receive wisdom </h3>
<p style='text-align: center;'> Powered by Radar Interactive </a>
"""
)
with gr.Group():
with gr.Row():
audio_out = gr.Audio(label="Spoken Answer", streaming=True, autoplay=True)
answer = gr.Textbox(label="Answer")
state = gr.State()
with gr.Row():
audio_in = gr.Audio(label="Speak your question", sources="microphone", type="filepath")
audio_in.stop_recording(generate_response, audio_in, [state, answer, audio_out])\
.then(fn=read_response, inputs=state, outputs=[answer, audio_out])
block.launch() |