Add new dependencies: openai-whisper, requests, and gradio to requirements.txt
Browse files- app.py +689 -208
- requirements.txt +4 -1
app.py
CHANGED
@@ -1,233 +1,714 @@
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import
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import gradio as gr
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from dotenv import load_dotenv
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load_dotenv()
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return audio_hat.detach().squeeze().cpu().numpy() # Always return CPU numpy array
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# Main generation function
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@spaces.GPU()
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def generate_speech(text, temperature=0.6, top_p=0.95, repetition_penalty=1.1, max_new_tokens=1200, voice="Elise", progress=gr.Progress()):
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if not text.strip():
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return None
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try:
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progress(0.3, "Generating speech tokens...")
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with torch.no_grad():
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audio_samples = redistribute_codes(code_list, snac_model)
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except Exception as e:
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print(f"Error
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return None
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with gr.Row():
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label="
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fn=
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inputs=
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outputs=[
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)
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if __name__ == "__main__":
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# -*- coding: utf-8 -*-
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# --- Imports ---
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import os
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import random
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import uuid
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import json
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import time
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import traceback
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import re
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import gradio as gr
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import torch
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import numpy as np
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import scipy.io.wavfile as wavfile
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import requests
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from dotenv import load_dotenv
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# --- Whisper Import ---
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try:
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import whisper
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print("Whisper library imported successfully.")
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except ImportError:
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print("ERROR: Whisper library not found. Please install it:")
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print("pip install -U openai-whisper")
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print("Ensure ffmpeg is also installed and in your system PATH.")
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exit(1)
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# --- SNAC Import ---
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try:
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from snac import SNAC
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except ImportError:
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print("ERROR: SNAC library not found. Please install it:")
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print("pip install git+https://github.com/hubertsiuzdak/snac.git")
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exit(1)
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# --- Load Environment Variables ---
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load_dotenv()
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# --- Unified Server Configuration ---
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SERVER_BASE_URL = os.getenv("SERVER_BASE_URL", "http://127.0.0.1:1234")
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# Ollama Configuration
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OLLAMA_API_ENDPOINT = f"{SERVER_BASE_URL}/v1/chat/completions"
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OLLAMA_MODEL = "dolphin3.0-llama3.1-8b-abliterated" # Updated model name
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OLLAMA_SYSTEM_PROMPT = "You in a chat with conscenting adults and you are an impatient midwestern foul mouthed 32 year old who just wants a link to github to get the code. You hate demos... You share a range of emotions in your responses of a single sentence where you keep asking in lightly profane creative ways for the link."
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# TTS Configuration
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TTS_API_ENDPOINT = f"{SERVER_BASE_URL}/v1/completions"
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TTS_MODEL = "mrrtmob/tts-khm-3"
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# --- Device Setup ---
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if torch.cuda.is_available():
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tts_device = "cuda"
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stt_device = "cuda"
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print("SNAC vocoder and Whisper STT will use CUDA if possible.")
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else:
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tts_device = "cpu"
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stt_device = "cpu"
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print("CUDA not available. SNAC vocoder and Whisper STT will use CPU.")
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# --- Model Loading ---
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print("Loading SNAC vocoder model...")
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snac_model = None
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try:
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snac_model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz")
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snac_model = snac_model.to(tts_device)
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snac_model.eval()
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print(f"SNAC vocoder loaded to {tts_device}")
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except Exception as e:
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print(f"Error loading SNAC model: {e}")
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print("Loading Whisper STT model...")
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WHISPER_MODEL_NAME = "base.en"
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whisper_model = None
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try:
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whisper_model = whisper.load_model(WHISPER_MODEL_NAME, device=stt_device)
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print(f"Whisper model '{WHISPER_MODEL_NAME}' loaded successfully.")
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except Exception as e:
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print(f"Error loading Whisper model: {e}")
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# --- Constants ---
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MAX_MAX_NEW_TOKENS = 4096
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DEFAULT_OLLAMA_MAX_TOKENS = -1 # Updated to match model's recommendation
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MAX_SEED = np.iinfo(np.int32).max
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ORPHEUS_MIN_ID = 10
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ORPHEUS_TOKENS_PER_LAYER = 4096
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ORPHEUS_N_LAYERS = 7
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ORPHEUS_MAX_ID = ORPHEUS_MIN_ID + (ORPHEUS_N_LAYERS * ORPHEUS_TOKENS_PER_LAYER)
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DEFAULT_OLLAMA_TEMP = 0.7
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DEFAULT_OLLAMA_TOP_P = 0.9
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DEFAULT_OLLAMA_TOP_K = 40
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DEFAULT_OLLAMA_REP_PENALTY = 1.1
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DEFAULT_TTS_TEMP = 0.4
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DEFAULT_TTS_TOP_P = 0.9
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DEFAULT_TTS_TOP_K = 40
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DEFAULT_TTS_REP_PENALTY = 1.1
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CONTEXT_TURN_LIMIT = 3
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# --- Utility Functions ---
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
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if randomize_seed:
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seed = random.randint(0, MAX_SEED)
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return seed
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def clean_chat_history(limited_chat_history):
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cleaned_ollama_format = []
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if not limited_chat_history:
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return []
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for user_msg_display, bot_msg_display in limited_chat_history:
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user_text = None
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if isinstance(user_msg_display, str):
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if user_msg_display.startswith("🎤 (Audio Input): "):
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user_text = user_msg_display.split("🎤 (Audio Input): ", 1)[1]
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elif user_msg_display.startswith(("@tara-tts ", "@tara-llm ")):
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user_text = user_msg_display.split(" ", 1)[1]
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else:
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user_text = user_msg_display
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elif isinstance(user_msg_display, tuple):
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if len(user_msg_display) > 1 and isinstance(user_msg_display[1], str):
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user_text = user_msg_display[1].replace("🎤: ", "")
|
121 |
+
elif isinstance(user_msg_display[0], str) and not user_msg_display[0].endswith((".wav", ".mp3")):
|
122 |
+
user_text = user_msg_display[0]
|
123 |
+
|
124 |
+
bot_text = None
|
125 |
+
if isinstance(bot_msg_display, tuple):
|
126 |
+
if len(bot_msg_display) > 1 and isinstance(bot_msg_display[1], str):
|
127 |
+
bot_text = bot_msg_display[1]
|
128 |
+
elif isinstance(bot_msg_display, str):
|
129 |
+
if not bot_msg_display.startswith(("[Error", "(Error", "Sorry,", "(No input", "Processing", "(TTS failed")):
|
130 |
+
bot_text = bot_msg_display
|
131 |
+
|
132 |
+
if user_text and user_text.strip():
|
133 |
+
cleaned_ollama_format.append({"role": "user", "content": user_text})
|
134 |
+
if bot_text and bot_text.strip():
|
135 |
+
cleaned_ollama_format.append({"role": "assistant", "content": bot_text})
|
136 |
+
return cleaned_ollama_format
|
137 |
+
|
138 |
+
# --- TTS Pipeline Functions ---
|
139 |
+
def parse_gguf_codes(response_text):
|
140 |
+
absolute_ids = []
|
141 |
+
matches = re.findall(r"<custom_token_(\d+)>", response_text)
|
142 |
+
if not matches:
|
143 |
+
return []
|
144 |
+
for number_str in matches:
|
145 |
+
try:
|
146 |
+
token_id = int(number_str)
|
147 |
+
if ORPHEUS_MIN_ID <= token_id < ORPHEUS_MAX_ID:
|
148 |
+
absolute_ids.append(token_id)
|
149 |
+
except ValueError:
|
150 |
+
continue
|
151 |
+
print(f" - Parsed {len(absolute_ids)} valid audio token IDs using regex.")
|
152 |
+
return absolute_ids
|
153 |
+
|
154 |
+
def redistribute_codes(absolute_code_list, target_snac_model):
|
155 |
+
if not absolute_code_list or target_snac_model is None:
|
156 |
+
return None
|
157 |
+
|
158 |
+
snac_device = next(target_snac_model.parameters()).device
|
159 |
+
layer_1, layer_2, layer_3 = [], [], []
|
160 |
+
num_tokens = len(absolute_code_list)
|
161 |
+
num_groups = num_tokens // ORPHEUS_N_LAYERS
|
162 |
|
163 |
+
if num_groups == 0:
|
|
|
|
|
|
|
|
|
|
|
164 |
return None
|
165 |
|
166 |
+
print(f" - Processing {num_groups} groups of {ORPHEUS_N_LAYERS} codes for SNAC...")
|
167 |
+
|
168 |
+
for i in range(num_groups):
|
169 |
+
base_idx = i * ORPHEUS_N_LAYERS
|
170 |
+
if base_idx + ORPHEUS_N_LAYERS > num_tokens:
|
171 |
+
break
|
172 |
+
|
173 |
+
group_codes = absolute_code_list[base_idx:base_idx + ORPHEUS_N_LAYERS]
|
174 |
+
processed_group = [None] * ORPHEUS_N_LAYERS
|
175 |
+
valid_group = True
|
176 |
+
|
177 |
+
for j, token_id in enumerate(group_codes):
|
178 |
+
if not (ORPHEUS_MIN_ID <= token_id < ORPHEUS_MAX_ID):
|
179 |
+
valid_group = False
|
180 |
+
break
|
181 |
+
|
182 |
+
layer_index = (token_id - ORPHEUS_MIN_ID) // ORPHEUS_TOKENS_PER_LAYER
|
183 |
+
code_index = (token_id - ORPHEUS_MIN_ID) % ORPHEUS_TOKENS_PER_LAYER
|
184 |
+
|
185 |
+
if layer_index != j:
|
186 |
+
valid_group = False
|
187 |
+
break
|
188 |
+
|
189 |
+
processed_group[j] = code_index
|
190 |
+
|
191 |
+
if not valid_group:
|
192 |
+
continue
|
193 |
+
|
194 |
+
try:
|
195 |
+
layer_1.append(processed_group[0])
|
196 |
+
layer_2.append(processed_group[1])
|
197 |
+
layer_3.append(processed_group[2])
|
198 |
+
layer_3.append(processed_group[3])
|
199 |
+
layer_2.append(processed_group[4])
|
200 |
+
layer_3.append(processed_group[5])
|
201 |
+
layer_3.append(processed_group[6])
|
202 |
+
except (IndexError, TypeError):
|
203 |
+
continue
|
204 |
+
|
205 |
try:
|
206 |
+
if not layer_1 or not layer_2 or not layer_3:
|
207 |
+
return None
|
208 |
+
|
209 |
+
print(f" - Final SNAC layer sizes: L1={len(layer_1)}, L2={len(layer_2)}, L3={len(layer_3)}")
|
210 |
+
|
211 |
+
codes = [
|
212 |
+
torch.tensor(layer_1, device=snac_device, dtype=torch.long).unsqueeze(0),
|
213 |
+
torch.tensor(layer_2, device=snac_device, dtype=torch.long).unsqueeze(0),
|
214 |
+
torch.tensor(layer_3, device=snac_device, dtype=torch.long).unsqueeze(0)
|
215 |
+
]
|
216 |
|
|
|
217 |
with torch.no_grad():
|
218 |
+
audio_hat = target_snac_model.decode(codes)
|
219 |
+
|
220 |
+
return audio_hat.detach().squeeze().cpu().numpy()
|
221 |
+
except Exception as e:
|
222 |
+
print(f"Error during tensor creation or SNAC decoding: {e}")
|
223 |
+
return None
|
224 |
+
|
225 |
+
def generate_speech_gguf(text, voice, tts_temperature, tts_top_p, tts_repetition_penalty, max_new_tokens_audio):
|
226 |
+
if not text.strip() or snac_model is None:
|
227 |
+
return None
|
|
|
228 |
|
229 |
+
print(f"Generating speech via TTS server for: '{text[:50]}...'")
|
230 |
+
start_time = time.time()
|
231 |
+
|
232 |
+
payload = {
|
233 |
+
"model": TTS_MODEL,
|
234 |
+
"prompt": f"<|audio|>{voice}: {text}<|eot_id|>",
|
235 |
+
"temperature": tts_temperature,
|
236 |
+
"top_p": tts_top_p,
|
237 |
+
"repeat_penalty": tts_repetition_penalty,
|
238 |
+
"max_tokens": max_new_tokens_audio,
|
239 |
+
"stop": ["<|eot_id|>", "<|audio|>"],
|
240 |
+
"stream": False
|
241 |
+
}
|
242 |
+
|
243 |
+
print(f" - Sending payload to {TTS_API_ENDPOINT} (Model: {TTS_MODEL})")
|
244 |
+
|
245 |
+
try:
|
246 |
+
headers = {"Content-Type": "application/json"}
|
247 |
+
response = requests.post(
|
248 |
+
TTS_API_ENDPOINT,
|
249 |
+
json=payload,
|
250 |
+
headers=headers,
|
251 |
+
timeout=180
|
252 |
+
)
|
253 |
+
response.raise_for_status()
|
254 |
+
response_json = response.json()
|
255 |
|
256 |
+
print(f" - Raw TTS response: {json.dumps(response_json, indent=2)[:200]}...")
|
|
|
257 |
|
258 |
+
if "choices" in response_json and len(response_json["choices"]) > 0:
|
259 |
+
raw_generated_text = response_json["choices"][0].get("text", "").strip()
|
260 |
+
if not raw_generated_text:
|
261 |
+
print("Error: Empty text in TTS response")
|
262 |
+
return None
|
263 |
+
|
264 |
+
req_time = time.time()
|
265 |
+
print(f" - TTS server request took {req_time - start_time:.2f}s")
|
266 |
+
|
267 |
+
absolute_id_list = parse_gguf_codes(raw_generated_text)
|
268 |
+
if not absolute_id_list:
|
269 |
+
print("Error: No valid audio codes parsed. Raw text:", raw_generated_text[:200])
|
270 |
+
return None
|
271 |
+
|
272 |
+
audio_samples = redistribute_codes(absolute_id_list, snac_model)
|
273 |
+
if audio_samples is None:
|
274 |
+
print("Error: Failed to generate audio samples from tokens")
|
275 |
+
return None
|
276 |
+
|
277 |
+
snac_time = time.time()
|
278 |
+
print(f" - Generated audio samples via SNAC, shape: {audio_samples.shape}")
|
279 |
+
print(f" - Total TTS generation time: {snac_time - start_time:.2f}s")
|
280 |
+
return (24000, audio_samples)
|
281 |
+
|
282 |
+
else:
|
283 |
+
print(f"Error: Unexpected TTS response format: {response_json}")
|
284 |
+
return None
|
285 |
+
|
286 |
+
except requests.exceptions.RequestException as e:
|
287 |
+
print(f"Error during request to TTS server: {e}")
|
288 |
+
return None
|
289 |
except Exception as e:
|
290 |
+
print(f"Error during TTS generation pipeline: {e}")
|
291 |
+
traceback.print_exc()
|
292 |
return None
|
293 |
+
|
294 |
+
# --- Ollama Communication Helper ---
|
295 |
+
def call_ollama_non_streaming(ollama_payload, generation_params):
|
296 |
+
final_response = "[Error: Default response]"
|
297 |
+
try:
|
298 |
+
payload = {
|
299 |
+
"model": OLLAMA_MODEL,
|
300 |
+
"messages": ollama_payload["messages"],
|
301 |
+
"temperature": generation_params.get('ollama_temperature', DEFAULT_OLLAMA_TEMP),
|
302 |
+
"top_p": generation_params.get('ollama_top_p', DEFAULT_OLLAMA_TOP_P),
|
303 |
+
"max_tokens": generation_params.get('ollama_max_new_tokens', DEFAULT_OLLAMA_MAX_TOKENS),
|
304 |
+
"repeat_penalty": generation_params.get('ollama_repetition_penalty', DEFAULT_OLLAMA_REP_PENALTY),
|
305 |
+
"stream": False
|
306 |
+
}
|
307 |
+
|
308 |
+
print(f" - Sending to {OLLAMA_API_ENDPOINT} with model {OLLAMA_MODEL}")
|
309 |
+
|
310 |
+
headers = {"Content-Type": "application/json"}
|
311 |
+
start_time = time.time()
|
312 |
+
response = requests.post(
|
313 |
+
OLLAMA_API_ENDPOINT,
|
314 |
+
json=payload,
|
315 |
+
headers=headers,
|
316 |
+
timeout=180
|
317 |
+
)
|
318 |
+
response.raise_for_status()
|
319 |
+
response_json = response.json()
|
320 |
+
end_time = time.time()
|
321 |
+
|
322 |
+
print(f" - LLM request took {end_time - start_time:.2f}s")
|
323 |
+
|
324 |
+
if "choices" in response_json and len(response_json["choices"]) > 0:
|
325 |
+
choice = response_json["choices"][0]
|
326 |
+
if "message" in choice:
|
327 |
+
final_response = choice["message"]["content"].strip()
|
328 |
+
elif "text" in choice:
|
329 |
+
final_response = choice["text"].strip()
|
330 |
+
else:
|
331 |
+
final_response = "[Error: Unexpected response format]"
|
332 |
+
else:
|
333 |
+
final_response = f"[Error: {response_json.get('error', 'Unknown error')}]"
|
334 |
+
|
335 |
+
except requests.exceptions.RequestException as e:
|
336 |
+
final_response = f"[Error connecting to LLM: {e}]"
|
337 |
+
except Exception as e:
|
338 |
+
final_response = f"[Unexpected Error: {e}]"
|
339 |
+
traceback.print_exc()
|
340 |
+
|
341 |
+
print(f" - LLM response: '{final_response[:100]}...'")
|
342 |
+
return final_response
|
343 |
+
|
344 |
+
# --- Main Gradio Backend Function ---
|
345 |
+
def process_input_blocks(
|
346 |
+
text_input: str, audio_input_path: str,
|
347 |
+
auto_prefix_tts_checkbox: bool,
|
348 |
+
auto_prefix_llm_checkbox: bool,
|
349 |
+
plain_llm_checkbox: bool,
|
350 |
+
ollama_max_new_tokens: int, ollama_temperature: float, ollama_top_p: float,
|
351 |
+
ollama_top_k: int, ollama_repetition_penalty: float,
|
352 |
+
tts_temperature: float, tts_top_p: float, tts_repetition_penalty: float,
|
353 |
+
chat_history: list
|
354 |
+
):
|
355 |
+
global whisper_model, snac_model
|
356 |
+
original_user_input_text = ""
|
357 |
+
user_display_input = None
|
358 |
+
text_to_process = ""
|
359 |
+
transcription_source = "text"
|
360 |
+
bot_response = ""
|
361 |
+
bot_audio_tuple = None
|
362 |
+
audio_filepath_to_clean = None
|
363 |
+
is_purely_text_input = False
|
364 |
+
prefix_to_add = None
|
365 |
+
force_plain_llm = False
|
366 |
+
|
367 |
+
# Handle Audio Input
|
368 |
+
if audio_input_path and whisper_model:
|
369 |
+
if os.path.isfile(audio_input_path):
|
370 |
+
audio_filepath_to_clean = audio_input_path
|
371 |
+
transcription_source = "voice"
|
372 |
+
print(f"Processing audio input: {audio_input_path}")
|
373 |
+
try:
|
374 |
+
stt_start_time = time.time()
|
375 |
+
result = whisper_model.transcribe(audio_input_path, fp16=(stt_device == 'cuda'))
|
376 |
+
original_user_input_text = result["text"].strip()
|
377 |
+
stt_end_time = time.time()
|
378 |
+
print(f" - Whisper transcription: '{original_user_input_text}' (took {stt_end_time - stt_start_time:.2f}s)")
|
379 |
+
user_display_input = f"🎤 (Audio Input): {original_user_input_text}"
|
380 |
+
text_to_process = original_user_input_text
|
381 |
+
|
382 |
+
# Check if transcription is already a command
|
383 |
+
known_prefixes = ["@tara-tts", "@jess-tts", "@leo-tts", "@leah-tts", "@dan-tts", "@mia-tts", "@zac-tts", "@zoe-tts",
|
384 |
+
"@tara-llm", "@jess-llm", "@leo-llm", "@leah-llm", "@dan-llm", "@mia-llm", "@zac-llm", "@zoe-llm"]
|
385 |
+
is_already_command = any(original_user_input_text.lower().startswith(p) for p in known_prefixes)
|
386 |
+
|
387 |
+
if not is_already_command:
|
388 |
+
if plain_llm_checkbox:
|
389 |
+
prefix_to_add = None
|
390 |
+
force_plain_llm = True
|
391 |
+
print(f" - Plain LLM checked. Processing audio as text input for LLM.")
|
392 |
+
elif auto_prefix_tts_checkbox:
|
393 |
+
prefix_to_add = "@tara-tts"
|
394 |
+
print(f" - Auto-prefix TTS checked. Applying to audio.")
|
395 |
+
elif auto_prefix_llm_checkbox:
|
396 |
+
prefix_to_add = "@tara-llm"
|
397 |
+
print(f" - Auto-prefix LLM checked. Applying to audio.")
|
398 |
+
else:
|
399 |
+
print(f" - No default prefix checkbox checked for audio. Processing as text for LLM.")
|
400 |
+
|
401 |
+
if prefix_to_add:
|
402 |
+
text_to_process = f"{prefix_to_add} {original_user_input_text}"
|
403 |
+
else:
|
404 |
+
print(f" - Transcribed audio is already a command '{original_user_input_text[:20]}...'.")
|
405 |
+
text_to_process = original_user_input_text
|
406 |
+
|
407 |
+
except Exception as e:
|
408 |
+
print(f"Error during Whisper transcription: {e}")
|
409 |
+
traceback.print_exc()
|
410 |
+
error_msg = f"[Error during local transcription: {e}]"
|
411 |
+
chat_history.append((f"🎤 (Audio Input Error: {audio_input_path})", error_msg))
|
412 |
+
if audio_filepath_to_clean and os.path.exists(audio_filepath_to_clean):
|
413 |
+
try:
|
414 |
+
os.remove(audio_filepath_to_clean)
|
415 |
+
except Exception as e_clean:
|
416 |
+
print(f"Warning: Could not clean up STT temp file {audio_filepath_to_clean}: {e_clean}")
|
417 |
+
return chat_history, None, None
|
418 |
+
else:
|
419 |
+
print(f"Received invalid audio path: {audio_input_path}, falling back to text.")
|
420 |
+
|
421 |
+
# Handle Text Input
|
422 |
+
if not text_to_process and text_input:
|
423 |
+
original_user_input_text = text_input.strip()
|
424 |
+
user_display_input = original_user_input_text
|
425 |
+
print(f"Processing text input: '{original_user_input_text}'")
|
426 |
+
transcription_source = "text"
|
427 |
+
text_to_process = original_user_input_text
|
428 |
+
|
429 |
+
known_prefixes = ["@tara-tts", "@jess-tts", "@leo-tts", "@leah-tts", "@dan-tts", "@mia-tts", "@zac-tts", "@zoe-tts",
|
430 |
+
"@tara-llm", "@jess-llm", "@leo-llm", "@leah-llm", "@dan-llm", "@mia-llm", "@zac-llm", "@zoe-llm"]
|
431 |
+
is_already_command = any(original_user_input_text.lower().startswith(p) for p in known_prefixes)
|
432 |
+
|
433 |
+
if not is_already_command:
|
434 |
+
if plain_llm_checkbox:
|
435 |
+
prefix_to_add = None
|
436 |
+
force_plain_llm = True
|
437 |
+
print(f" - Plain LLM checked. Processing text input for LLM.")
|
438 |
+
elif auto_prefix_tts_checkbox:
|
439 |
+
prefix_to_add = "@tara-tts"
|
440 |
+
print(f" - Auto-prefix TTS checked. Applying to text.")
|
441 |
+
elif auto_prefix_llm_checkbox:
|
442 |
+
prefix_to_add = "@tara-llm"
|
443 |
+
print(f" - Auto-prefix LLM checked. Applying to text.")
|
444 |
+
else:
|
445 |
+
print(f" - No default prefix checkbox enabled for text input.")
|
446 |
+
else:
|
447 |
+
print(f" - User provided command in text '{original_user_input_text[:20]}...', not auto-prepending.")
|
448 |
+
|
449 |
+
if prefix_to_add:
|
450 |
+
text_to_process = f"{prefix_to_add} {original_user_input_text}"
|
451 |
+
|
452 |
+
# Cleanup audio file
|
453 |
+
if audio_filepath_to_clean and os.path.exists(audio_filepath_to_clean):
|
454 |
+
try:
|
455 |
+
os.remove(audio_filepath_to_clean)
|
456 |
+
print(f" - Cleaned up temporary STT audio file: {audio_filepath_to_clean}")
|
457 |
+
except Exception as e_clean:
|
458 |
+
print(f"Warning: Could not clean up temp STT audio file {audio_filepath_to_clean}: {e_clean}")
|
459 |
+
|
460 |
+
if not text_to_process:
|
461 |
+
print("No valid text or audio input to process.")
|
462 |
+
return chat_history, None, None
|
463 |
+
|
464 |
+
chat_history.append((user_display_input, None))
|
465 |
+
|
466 |
+
# Process Input Text
|
467 |
+
lower_text = text_to_process.lower()
|
468 |
+
print(f" - Routing query ({transcription_source}): '{text_to_process[:100]}...'")
|
469 |
|
470 |
+
all_voices = ["tara", "jess", "leo", "leah", "dan", "mia", "zac", "zoe"]
|
471 |
+
tts_tags = {f"@{voice}-tts": voice for voice in all_voices}
|
472 |
+
llm_tags = {f"@{voice}-llm": voice for voice in all_voices}
|
473 |
+
|
474 |
+
final_bot_message = None
|
475 |
+
|
476 |
+
try:
|
477 |
+
matched_tts = False
|
478 |
+
matched_llm_tts = False
|
479 |
+
|
480 |
+
# Check Branches
|
481 |
+
if not force_plain_llm:
|
482 |
+
# Branch 1: Direct TTS
|
483 |
+
for tag, voice in tts_tags.items():
|
484 |
+
if lower_text.startswith(tag):
|
485 |
+
matched_tts = True
|
486 |
+
text_to_speak = text_to_process[len(tag):].strip()
|
487 |
+
print(f" - Direct TTS request for voice '{voice}': '{text_to_speak[:50]}...'")
|
488 |
+
if snac_model is None:
|
489 |
+
raise ValueError("SNAC vocoder not loaded.")
|
490 |
+
audio_output = generate_speech_gguf(
|
491 |
+
text_to_speak, voice,
|
492 |
+
tts_temperature, tts_top_p, tts_repetition_penalty,
|
493 |
+
MAX_MAX_NEW_TOKENS
|
494 |
+
)
|
495 |
+
if audio_output:
|
496 |
+
sample_rate, audio_data = audio_output
|
497 |
+
if audio_data.dtype != np.int16:
|
498 |
+
if np.issubdtype(audio_data.dtype, np.floating):
|
499 |
+
max_val = np.max(np.abs(audio_data))
|
500 |
+
audio_data = np.int16(audio_data/max_val*32767) if max_val > 1e-6 else np.zeros_like(audio_data, dtype=np.int16)
|
501 |
+
else:
|
502 |
+
audio_data = audio_data.astype(np.int16)
|
503 |
+
temp_dir = "temp_audio_files"
|
504 |
+
os.makedirs(temp_dir, exist_ok=True)
|
505 |
+
temp_audio_path = os.path.join(temp_dir, f"temp_audio_{uuid.uuid4().hex}.wav")
|
506 |
+
wavfile.write(temp_audio_path, sample_rate, audio_data)
|
507 |
+
print(f" - Saved TTS audio: {temp_audio_path}")
|
508 |
+
final_bot_message = (temp_audio_path, None)
|
509 |
+
else:
|
510 |
+
final_bot_message = f"Sorry, couldn't generate speech for '{text_to_speak[:50]}...'."
|
511 |
+
break
|
512 |
+
|
513 |
+
# Branch 2: LLM + TTS
|
514 |
+
if not matched_tts:
|
515 |
+
for tag, voice in llm_tags.items():
|
516 |
+
if lower_text.startswith(tag):
|
517 |
+
matched_llm_tts = True
|
518 |
+
prompt_for_llm = text_to_process[len(tag):].strip()
|
519 |
+
print(f" - LLM+TTS request for voice '{voice}': '{prompt_for_llm[:75]}...'")
|
520 |
+
if snac_model is None:
|
521 |
+
raise ValueError("SNAC vocoder not loaded.")
|
522 |
+
|
523 |
+
history_before_current = chat_history[:-1]
|
524 |
+
limited_history_turns = history_before_current[-CONTEXT_TURN_LIMIT:]
|
525 |
+
cleaned_hist_for_llm = clean_chat_history(limited_history_turns)
|
526 |
+
|
527 |
+
messages = [
|
528 |
+
{"role": "system", "content": OLLAMA_SYSTEM_PROMPT}
|
529 |
+
] + cleaned_hist_for_llm + [
|
530 |
+
{"role": "user", "content": prompt_for_llm}
|
531 |
+
]
|
532 |
+
|
533 |
+
llm_params = {
|
534 |
+
'ollama_temperature': ollama_temperature,
|
535 |
+
'ollama_top_p': ollama_top_p,
|
536 |
+
'ollama_top_k': ollama_top_k,
|
537 |
+
'ollama_max_new_tokens': ollama_max_new_tokens,
|
538 |
+
'ollama_repetition_penalty': ollama_repetition_penalty
|
539 |
+
}
|
540 |
+
|
541 |
+
llm_response_text = call_ollama_non_streaming(
|
542 |
+
{"messages": messages},
|
543 |
+
llm_params
|
544 |
+
)
|
545 |
+
|
546 |
+
if llm_response_text and not llm_response_text.startswith("[Error"):
|
547 |
+
audio_output = generate_speech_gguf(
|
548 |
+
llm_response_text, voice,
|
549 |
+
tts_temperature, tts_top_p, tts_repetition_penalty,
|
550 |
+
MAX_MAX_NEW_TOKENS
|
551 |
+
)
|
552 |
+
if audio_output:
|
553 |
+
sample_rate, audio_data = audio_output
|
554 |
+
if audio_data.dtype != np.int16:
|
555 |
+
if np.issubdtype(audio_data.dtype, np.floating):
|
556 |
+
max_val = np.max(np.abs(audio_data))
|
557 |
+
audio_data = np.int16(audio_data/max_val*32767) if max_val > 1e-6 else np.zeros_like(audio_data, dtype=np.int16)
|
558 |
+
else:
|
559 |
+
audio_data = audio_data.astype(np.int16)
|
560 |
+
temp_dir = "temp_audio_files"
|
561 |
+
os.makedirs(temp_dir, exist_ok=True)
|
562 |
+
temp_audio_path = os.path.join(temp_dir, f"temp_audio_{uuid.uuid4().hex}.wav")
|
563 |
+
wavfile.write(temp_audio_path, sample_rate, audio_data)
|
564 |
+
print(f" - Saved LLM+TTS audio: {temp_audio_path}")
|
565 |
+
final_bot_message = (temp_audio_path, llm_response_text)
|
566 |
+
else:
|
567 |
+
print("Warning: TTS generation failed...")
|
568 |
+
final_bot_message = f"{llm_response_text}\n\n(TTS failed...)"
|
569 |
+
else:
|
570 |
+
final_bot_message = llm_response_text
|
571 |
+
break
|
572 |
+
|
573 |
+
# Branch 3: Plain LLM
|
574 |
+
if force_plain_llm or (not matched_tts and not matched_llm_tts):
|
575 |
+
if force_plain_llm:
|
576 |
+
print(f" - Plain LLM chat mode forced by checkbox...")
|
577 |
+
else:
|
578 |
+
print(f" - Default text chat (no command prefix detected/added)...")
|
579 |
+
|
580 |
+
history_before_current = chat_history[:-1]
|
581 |
+
limited_history_turns = history_before_current[-CONTEXT_TURN_LIMIT:]
|
582 |
+
cleaned_hist_for_llm = clean_chat_history(limited_history_turns)
|
583 |
+
|
584 |
+
messages = [
|
585 |
+
{"role": "system", "content": OLLAMA_SYSTEM_PROMPT}
|
586 |
+
] + cleaned_hist_for_llm + [
|
587 |
+
{"role": "user", "content": original_user_input_text}
|
588 |
+
]
|
589 |
+
|
590 |
+
llm_params = {
|
591 |
+
'ollama_temperature': ollama_temperature,
|
592 |
+
'ollama_top_p': ollama_top_p,
|
593 |
+
'ollama_top_k': ollama_top_k,
|
594 |
+
'ollama_max_new_tokens': ollama_max_new_tokens,
|
595 |
+
'ollama_repetition_penalty': ollama_repetition_penalty
|
596 |
+
}
|
597 |
+
|
598 |
+
final_bot_message = call_ollama_non_streaming(
|
599 |
+
{"messages": messages},
|
600 |
+
llm_params
|
601 |
)
|
602 |
+
|
603 |
+
except Exception as e:
|
604 |
+
print(f"Error during processing: {e}")
|
605 |
+
traceback.print_exc()
|
606 |
+
final_bot_message = f"[An unexpected error occurred: {e}]"
|
607 |
+
|
608 |
+
chat_history[-1] = (user_display_input, final_bot_message)
|
609 |
+
return chat_history, None, None
|
610 |
+
|
611 |
+
# --- Gradio Interface ---
|
612 |
+
def update_prefix_checkboxes(selected_checkbox_label):
|
613 |
+
if selected_checkbox_label == "tts":
|
614 |
+
return gr.update(value=True), gr.update(value=False), gr.update(value=False)
|
615 |
+
elif selected_checkbox_label == "llm":
|
616 |
+
return gr.update(value=False), gr.update(value=True), gr.update(value=False)
|
617 |
+
elif selected_checkbox_label == "plain":
|
618 |
+
return gr.update(value=False), gr.update(value=False), gr.update(value=True)
|
619 |
+
else:
|
620 |
+
return gr.update(), gr.update(), gr.update()
|
621 |
+
|
622 |
+
print("Setting up Gradio Interface with gr.Blocks...")
|
623 |
+
theme_to_use = None
|
624 |
+
|
625 |
+
with gr.Blocks(theme=theme_to_use) as demo:
|
626 |
+
gr.Markdown(f"# Orpheus Edge 🎤 ({OLLAMA_MODEL}) Chat & TTS")
|
627 |
+
|
628 |
+
chatbot = gr.Chatbot(label="Chat History", height=500)
|
629 |
|
630 |
with gr.Row():
|
631 |
+
with gr.Column(scale=3):
|
632 |
+
text_input_box = gr.Textbox(label="Type your message or use microphone", lines=2)
|
633 |
+
with gr.Column(scale=1):
|
634 |
+
audio_input_mic = gr.Audio(label="Record Audio Input", type="filepath")
|
635 |
|
636 |
+
with gr.Row():
|
637 |
+
auto_prefix_tts_checkbox = gr.Checkbox(label="Default to TTS (@tara-tts)", value=True, elem_id="cb_tts")
|
638 |
+
auto_prefix_llm_checkbox = gr.Checkbox(label="Default to LLM+TTS (@tara-llm)", value=False, elem_id="cb_llm")
|
639 |
+
plain_llm_checkbox = gr.Checkbox(label="Plain LLM Chat (Text Out)", value=False, elem_id="cb_plain")
|
|
|
640 |
|
641 |
+
with gr.Row():
|
642 |
+
submit_button = gr.Button("Send / Submit")
|
643 |
+
clear_button = gr.ClearButton([text_input_box, audio_input_mic, chatbot])
|
644 |
+
|
645 |
+
with gr.Accordion("Generation Parameters", open=False):
|
646 |
+
gr.Markdown("### LLM Parameters")
|
647 |
+
ollama_max_new_tokens_slider = gr.Slider(label="Max New Tokens", minimum=32, maximum=4096, step=32, value=DEFAULT_OLLAMA_MAX_TOKENS)
|
648 |
+
ollama_temperature_slider = gr.Slider(label="Temperature", minimum=0.0, maximum=2.0, step=0.05, value=DEFAULT_OLLAMA_TEMP)
|
649 |
+
ollama_top_p_slider = gr.Slider(label="Top-p", minimum=0.05, maximum=1.0, step=0.05, value=DEFAULT_OLLAMA_TOP_P)
|
650 |
+
ollama_top_k_slider = gr.Slider(label="Top-k", minimum=1, maximum=100, step=1, value=DEFAULT_OLLAMA_TOP_K)
|
651 |
+
ollama_repetition_penalty_slider = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.05, value=DEFAULT_OLLAMA_REP_PENALTY)
|
652 |
+
|
653 |
+
gr.Markdown("---")
|
654 |
+
gr.Markdown("### TTS Parameters")
|
655 |
+
tts_temperature_slider = gr.Slider(label="Temperature", minimum=0.0, maximum=2.0, step=0.05, value=DEFAULT_TTS_TEMP)
|
656 |
+
tts_top_p_slider = gr.Slider(label="Top-p", minimum=0.05, maximum=1.0, step=0.05, value=DEFAULT_TTS_TOP_P)
|
657 |
+
tts_repetition_penalty_slider = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.05, value=DEFAULT_TTS_REP_PENALTY)
|
658 |
|
659 |
+
param_inputs = [
|
660 |
+
ollama_max_new_tokens_slider, ollama_temperature_slider, ollama_top_p_slider,
|
661 |
+
ollama_top_k_slider, ollama_repetition_penalty_slider,
|
662 |
+
tts_temperature_slider, tts_top_p_slider, tts_repetition_penalty_slider
|
663 |
+
]
|
664 |
+
|
665 |
+
auto_prefix_tts_checkbox.change(
|
666 |
+
lambda: update_prefix_checkboxes("tts"),
|
667 |
+
None,
|
668 |
+
[auto_prefix_tts_checkbox, auto_prefix_llm_checkbox, plain_llm_checkbox]
|
669 |
+
)
|
670 |
+
auto_prefix_llm_checkbox.change(
|
671 |
+
lambda: update_prefix_checkboxes("llm"),
|
672 |
+
None,
|
673 |
+
[auto_prefix_tts_checkbox, auto_prefix_llm_checkbox, plain_llm_checkbox]
|
674 |
)
|
675 |
+
plain_llm_checkbox.change(
|
676 |
+
lambda: update_prefix_checkboxes("plain"),
|
677 |
+
None,
|
678 |
+
[auto_prefix_tts_checkbox, auto_prefix_llm_checkbox, plain_llm_checkbox]
|
679 |
+
)
|
680 |
+
|
681 |
+
all_inputs = [
|
682 |
+
text_input_box, audio_input_mic,
|
683 |
+
auto_prefix_tts_checkbox, auto_prefix_llm_checkbox, plain_llm_checkbox
|
684 |
+
] + param_inputs + [chatbot]
|
685 |
|
686 |
+
submit_button.click(
|
687 |
+
fn=process_input_blocks,
|
688 |
+
inputs=all_inputs,
|
689 |
+
outputs=[chatbot, text_input_box, audio_input_mic]
|
690 |
+
)
|
691 |
+
text_input_box.submit(
|
692 |
+
fn=process_input_blocks,
|
693 |
+
inputs=all_inputs,
|
694 |
+
outputs=[chatbot, text_input_box, audio_input_mic]
|
695 |
)
|
696 |
+
|
697 |
+
# --- Application Entry Point ---
|
698 |
if __name__ == "__main__":
|
699 |
+
print("-" * 50)
|
700 |
+
print(f"Launching Gradio {gr.__version__} Interface")
|
701 |
+
print(f"Whisper STT Model: {WHISPER_MODEL_NAME} on {stt_device}")
|
702 |
+
print(f"SNAC Vocoder loaded to {tts_device}")
|
703 |
+
print(f"Server URL: {SERVER_BASE_URL}")
|
704 |
+
print(f"LLM Model: {OLLAMA_MODEL}")
|
705 |
+
print(f"TTS Model: {TTS_MODEL}")
|
706 |
+
print("-" * 50)
|
707 |
+
print("Default Parameters:")
|
708 |
+
print(f" LLM: Temp={DEFAULT_OLLAMA_TEMP}, TopP={DEFAULT_OLLAMA_TOP_P}")
|
709 |
+
print(f" TTS: Temp={DEFAULT_TTS_TEMP}, TopP={DEFAULT_TTS_TOP_P}")
|
710 |
+
print("-" * 50)
|
711 |
+
print("Ensure your LM Studio server is running with both models loaded")
|
712 |
+
os.makedirs("temp_audio_files", exist_ok=True)
|
713 |
+
demo.launch(share=False)
|
714 |
+
print("Gradio Interface launched. Press Ctrl+C to stop.")
|
requirements.txt
CHANGED
@@ -2,4 +2,7 @@ snac
|
|
2 |
python-dotenv
|
3 |
transformers
|
4 |
torch
|
5 |
-
spaces
|
|
|
|
|
|
|
|
2 |
python-dotenv
|
3 |
transformers
|
4 |
torch
|
5 |
+
spaces
|
6 |
+
openai-whisper
|
7 |
+
requests
|
8 |
+
gradio
|