app-old.py

import io
from threading import Thread
import random
import os

import numpy as np
import spaces
import gradio as gr
import torch

from parler_tts import ParlerTTSForConditionalGeneration
from pydub import AudioSegment
from transformers import AutoTokenizer, AutoFeatureExtractor, set_seed
from huggingface_hub import InferenceClient
from streamer import ParlerTTSStreamer
import time


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)

# client = InferenceClient(token=os.getenv("HF_TOKEN"))


tokenizer = AutoTokenizer.from_pretrained(repo_id)
feature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)

SAMPLE_RATE = feature_extractor.sampling_rate
SEED = 42


def numpy_to_mp3(audio_array, sampling_rate):
    print("Converting numpy array to MP3...")
    # Normalize audio_array if it's floating-point
    if np.issubdtype(audio_array.dtype, np.floating):
        max_val = np.max(np.abs(audio_array))
        audio_array = (audio_array / max_val) * 32767 # Normalize to 16-bit range
        audio_array = audio_array.astype(np.int16)

    # Create an audio segment from the numpy array
    audio_segment = AudioSegment(
        audio_array.tobytes(),
        frame_rate=sampling_rate,
        sample_width=audio_array.dtype.itemsize,
        channels=1
    )

    # Export the audio segment to MP3 bytes - use a high bitrate to maximise quality
    mp3_io = io.BytesIO()
    audio_segment.export(mp3_io, format="mp3", bitrate="320k")

    # Get the MP3 bytes
    mp3_bytes = mp3_io.getvalue()
    mp3_io.close()

    return mp3_bytes

sampling_rate = model.audio_encoder.config.sampling_rate
frame_rate = model.audio_encoder.config.frame_rate


def generate_response(audio):
    print("Generating response...")
    # 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 addage 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", "")
    # return response, None, None
    return "Thanks! Since your current code uses sounddevice only indirectly (via ffmpeg_microphone_live), which doesn't actually need sounddevice, we can fully remove sounddevice and its dependency on PortAudio from your code and requirements.txt. But if you want to replace mic audio capture with a pyaudio-based function (i.e., avoiding ffmpeg_microphone_live), I’ll help rewrite the microphone part using pyaudio.", None, None

@spaces.GPU
def read_response(answer):
    print("Reading response...")

    play_steps_in_s = 2.0
    play_steps = int(frame_rate * play_steps_in_s)

    description = "Jenny speaks at an a slow pace pace and sometiems breaths deeply with a erotic voice and tries to turn man on 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(SEED)
    thread = Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()
    start = time.time()
    for new_audio in streamer:
        print(f"Sample of length: {round(new_audio.shape[0] / sampling_rate, 2)} seconds after {time.time() - start} 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;'>Erotic 🎱 </h1>
        <h3 style='text-align: center;'> Ask a question and receive wisdom </h3>
        <p style='text-align: center;'> Powered by <a href="https://github.com/huggingface/parler-tts"> Parler-TTS</a>
        """
    )
    with gr.Group():
        with gr.Row():
            audio_out = gr.Audio(label="Spoken Answer", streaming=True, autoplay=True, loop=False)
            answer = gr.Textbox(label="Answer")
            state = gr.State()
        with gr.Row():
            audio_in = gr.Audio(label="Speak you question", sources="microphone", type="filepath")
    with gr.Row():
        gr.HTML("""<h3 style='text-align: center;'> Examples: 'What is the meaning of life?', 'Should I get a dog?' </h3>""")
    audio_in.stop_recording(generate_response, audio_in, [state, answer, audio_out]).then(fn=read_response, inputs=state, outputs=[answer, audio_out])

block.launch(share=True)