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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 | |
| 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() |