Update app.py

app.py

@@ -23,6 +23,60 @@ def generate_response(audio):
     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(