Create app.py

app.py

@@ -0,0 +1,133 @@
+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):
+    # 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):
+    # 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 "test response", None, None
+
+@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(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;'> Magic 8 Ball 🎱 </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()