remove unneded testfile

mistral.ipynb

@@ -1,578 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "f8bdd950-1b95-4088-890a-94417292f6e1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[nltk_data] Downloading package punkt to /home/gorkem/nltk_data...\n",
-      "[nltk_data]   Package punkt is already up-to-date!\n",
-      "2023-10-13 00:33:39.399490: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading if not downloaded Coqui XTTS V1\n",
-      " > tts_models/multilingual/multi-dataset/xtts_v1 is already downloaded.\n",
-      " > Using model: xtts\n",
-      "XTTS downloaded\n",
-      "Loading XTTS\n",
-      "[2023-10-13 00:34:12,573] [INFO] [logging.py:93:log_dist] [Rank -1] DeepSpeed info: version=0.8.3+f1e4fb0b, git-hash=f1e4fb0b, git-branch=HEAD\n",
-      "[2023-10-13 00:34:12,587] [WARNING] [config_utils.py:75:_process_deprecated_field] Config parameter replace_method is deprecated. This parameter is no longer needed, please remove from your call to DeepSpeed-inference\n",
-      "[2023-10-13 00:34:12,589] [WARNING] [config_utils.py:75:_process_deprecated_field] Config parameter mp_size is deprecated use tensor_parallel.tp_size instead\n",
-      "[2023-10-13 00:34:12,590] [INFO] [logging.py:93:log_dist] [Rank -1] quantize_bits = 8 mlp_extra_grouping = False, quantize_groups = 1\n",
-      "[2023-10-13 00:34:12,854] [INFO] [logging.py:93:log_dist] [Rank -1] DeepSpeed-Inference config: {'layer_id': 0, 'hidden_size': 1024, 'intermediate_size': 4096, 'heads': 16, 'num_hidden_layers': -1, 'fp16': False, 'pre_layer_norm': True, 'local_rank': -1, 'stochastic_mode': False, 'epsilon': 1e-05, 'mp_size': 1, 'q_int8': False, 'scale_attention': True, 'triangular_masking': True, 'local_attention': False, 'window_size': 1, 'rotary_dim': -1, 'rotate_half': False, 'rotate_every_two': True, 'return_tuple': True, 'mlp_after_attn': True, 'mlp_act_func_type': <ActivationFuncType.GELU: 1>, 'specialized_mode': False, 'training_mp_size': 1, 'bigscience_bloom': False, 'max_out_tokens': 1024, 'scale_attn_by_inverse_layer_idx': False, 'enable_qkv_quantization': False, 'use_mup': False, 'return_single_tuple': False}\n",
-      "Done loading TTS\n",
-      "Loaded as API: https://sanchit-gandhi-whisper-jax.hf.space/ ✔\n"
-     ]
-    }
-   ],
-   "source": [
-    "from __future__ import annotations\n",
-    "\n",
-    "import os\n",
-    "# By using XTTS you agree to CPML license https://coqui.ai/cpml\n",
-    "os.environ[\"COQUI_TOS_AGREED\"] = \"1\"\n",
-    "\n",
-    "import gradio as gr\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import nltk  # we'll use this to split into sentences\n",
-    "nltk.download('punkt')\n",
-    "import uuid\n",
-    "\n",
-    "import librosa\n",
-    "import torchaudio\n",
-    "from TTS.api import TTS\n",
-    "from TTS.tts.configs.xtts_config import XttsConfig\n",
-    "from TTS.tts.models.xtts import Xtts\n",
-    "from TTS.utils.generic_utils import get_user_data_dir\n",
-    "\n",
-    "# This will trigger downloading model\n",
-    "print(\"Downloading if not downloaded Coqui XTTS V1\")\n",
-    "tts = TTS(\"tts_models/multilingual/multi-dataset/xtts_v1\")\n",
-    "del tts\n",
-    "print(\"XTTS downloaded\")\n",
-    "\n",
-    "print(\"Loading XTTS\")\n",
-    "#Below will use model directly for inference\n",
-    "model_path = os.path.join(get_user_data_dir(\"tts\"), \"tts_models--multilingual--multi-dataset--xtts_v1\")\n",
-    "config = XttsConfig()\n",
-    "config.load_json(os.path.join(model_path, \"config.json\"))\n",
-    "model = Xtts.init_from_config(config)\n",
-    "model.load_checkpoint(\n",
-    "    config,\n",
-    "    checkpoint_path=os.path.join(model_path, \"model.pth\"),\n",
-    "    vocab_path=os.path.join(model_path, \"vocab.json\"),\n",
-    "    eval=True,\n",
-    "    use_deepspeed=True\n",
-    ")\n",
-    "model.cuda()\n",
-    "print(\"Done loading TTS\")\n",
-    "\n",
-    "\n",
-    "title = \"Voice chat with Mistral 7B Instruct\"\n",
-    "\n",
-    "DESCRIPTION = \"\"\"# Voice chat with Mistral 7B Instruct\"\"\"\n",
-    "css = \"\"\".toast-wrap { display: none !important } \"\"\"\n",
-    "\n",
-    "from huggingface_hub import HfApi\n",
-    "HF_TOKEN = os.environ.get(\"HF_TOKEN\")\n",
-    "# will use api to restart space on a unrecoverable error\n",
-    "api = HfApi(token=HF_TOKEN)\n",
-    "\n",
-    "repo_id = \"ylacombe/voice-chat-with-lama\"\n",
-    "\n",
-    "system_message = \"\\nYou are a helpful, respectful and honest assistant. Your answers are short, ideally a few words long, if it is possible. Always answer as helpfully as possible, while being safe.\\n\\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\"\n",
-    "temperature = 0.9\n",
-    "top_p = 0.6\n",
-    "repetition_penalty = 1.2\n",
-    "\n",
-    "\n",
-    "import gradio as gr\n",
-    "import os\n",
-    "import time\n",
-    "\n",
-    "import gradio as gr\n",
-    "from transformers import pipeline\n",
-    "import numpy as np\n",
-    "\n",
-    "from gradio_client import Client\n",
-    "from huggingface_hub import InferenceClient\n",
-    "\n",
-    "\n",
-    "# This client is down\n",
-    "#whisper_client = Client(\"https://sanchit-gandhi-whisper-large-v2.hf.space/\")\n",
-    "# Replacement whisper client, it may be time limited\n",
-    "whisper_client = Client(\"https://sanchit-gandhi-whisper-jax.hf.space\")\n",
-    "text_client = InferenceClient(\n",
-    "    \"mistralai/Mistral-7B-Instruct-v0.1\"\n",
-    ")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d8687cd2-e989-4db9-b16a-04ad9460e6f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Running on local URL:  http://127.0.0.1:7861\n",
-      "\n",
-      "To create a public link, set `share=True` in `launch()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div><iframe src=\"http://127.0.0.1:7861/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ERROR: Too many requests on mistral client\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/queueing.py\", line 388, in call_prediction\n",
-      "    output = await route_utils.call_process_api(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/route_utils.py\", line 219, in call_process_api\n",
-      "    output = await app.get_blocks().process_api(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1437, in process_api\n",
-      "    result = await self.call_function(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1123, in call_function\n",
-      "    prediction = await utils.async_iteration(iterator)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 503, in async_iteration\n",
-      "    return await iterator.__anext__()\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 496, in __anext__\n",
-      "    return await anyio.to_thread.run_sync(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
-      "    return await get_asynclib().run_sync_in_worker_thread(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
-      "    return await future\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
-      "    result = context.run(func, *args)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 479, in run_sync_iterator_async\n",
-      "    return next(iterator)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 629, in gen_wrapper\n",
-      "    yield from f(*args, **kwargs)\n",
-      "  File \"/tmp/ipykernel_8679/550220560.py\", line 134, in generate_speech\n",
-      "    text_to_generate = history[-1][1]\n",
-      "TypeError: 'NoneType' object is not subscriptable\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ERROR: Too many requests on mistral client\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/queueing.py\", line 388, in call_prediction\n",
-      "    output = await route_utils.call_process_api(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/route_utils.py\", line 219, in call_process_api\n",
-      "    output = await app.get_blocks().process_api(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1437, in process_api\n",
-      "    result = await self.call_function(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1123, in call_function\n",
-      "    prediction = await utils.async_iteration(iterator)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 503, in async_iteration\n",
-      "    return await iterator.__anext__()\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 496, in __anext__\n",
-      "    return await anyio.to_thread.run_sync(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
-      "    return await get_asynclib().run_sync_in_worker_thread(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
-      "    return await future\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
-      "    result = context.run(func, *args)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 479, in run_sync_iterator_async\n",
-      "    return next(iterator)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 629, in gen_wrapper\n",
-      "    yield from f(*args, **kwargs)\n",
-      "  File \"/tmp/ipykernel_8679/550220560.py\", line 134, in generate_speech\n",
-      "    text_to_generate = history[-1][1]\n",
-      "TypeError: 'NoneType' object is not subscriptable\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ERROR: Too many requests on mistral client\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/queueing.py\", line 388, in call_prediction\n",
-      "    output = await route_utils.call_process_api(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/route_utils.py\", line 219, in call_process_api\n",
-      "    output = await app.get_blocks().process_api(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1437, in process_api\n",
-      "    result = await self.call_function(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1123, in call_function\n",
-      "    prediction = await utils.async_iteration(iterator)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 503, in async_iteration\n",
-      "    return await iterator.__anext__()\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 496, in __anext__\n",
-      "    return await anyio.to_thread.run_sync(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
-      "    return await get_asynclib().run_sync_in_worker_thread(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
-      "    return await future\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
-      "    result = context.run(func, *args)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 479, in run_sync_iterator_async\n",
-      "    return next(iterator)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 629, in gen_wrapper\n",
-      "    yield from f(*args, **kwargs)\n",
-      "  File \"/tmp/ipykernel_8679/550220560.py\", line 134, in generate_speech\n",
-      "    text_to_generate = history[-1][1]\n",
-      "TypeError: 'NoneType' object is not subscriptable\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ERROR: Too many requests on mistral client\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Traceback (most recent call last):\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/queueing.py\", line 388, in call_prediction\n",
-      "    output = await route_utils.call_process_api(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/route_utils.py\", line 219, in call_process_api\n",
-      "    output = await app.get_blocks().process_api(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1437, in process_api\n",
-      "    result = await self.call_function(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/blocks.py\", line 1123, in call_function\n",
-      "    prediction = await utils.async_iteration(iterator)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 503, in async_iteration\n",
-      "    return await iterator.__anext__()\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 496, in __anext__\n",
-      "    return await anyio.to_thread.run_sync(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/to_thread.py\", line 31, in run_sync\n",
-      "    return await get_asynclib().run_sync_in_worker_thread(\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 937, in run_sync_in_worker_thread\n",
-      "    return await future\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/anyio/_backends/_asyncio.py\", line 867, in run\n",
-      "    result = context.run(func, *args)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 479, in run_sync_iterator_async\n",
-      "    return next(iterator)\n",
-      "  File \"/home/gorkem/.local/lib/python3.10/site-packages/gradio/utils.py\", line 629, in gen_wrapper\n",
-      "    yield from f(*args, **kwargs)\n",
-      "  File \"/tmp/ipykernel_8679/550220560.py\", line 134, in generate_speech\n",
-      "    text_to_generate = history[-1][1]\n",
-      "TypeError: 'NoneType' object is not subscriptable\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "###### COQUI TTS FUNCTIONS ######\n",
-    "def get_latents(speaker_wav):\n",
-    "    # create as function as we can populate here with voice cleanup/filtering\n",
-    "    gpt_cond_latent, diffusion_conditioning, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav)\n",
-    "    return gpt_cond_latent, diffusion_conditioning, speaker_embedding\n",
-    "\n",
-    "\n",
-    "def format_prompt(message, history):\n",
-    "  prompt = \"<s>\"\n",
-    "  for user_prompt, bot_response in history:\n",
-    "    prompt += f\"[INST] {user_prompt} [/INST]\"\n",
-    "    prompt += f\" {bot_response}</s> \"\n",
-    "  prompt += f\"[INST] {message} [/INST]\"\n",
-    "  return prompt\n",
-    "\n",
-    "def generate(\n",
-    "    prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,\n",
-    "):\n",
-    "    temperature = float(temperature)\n",
-    "    if temperature < 1e-2:\n",
-    "        temperature = 1e-2\n",
-    "    top_p = float(top_p)\n",
-    "\n",
-    "    generate_kwargs = dict(\n",
-    "        temperature=temperature,\n",
-    "        max_new_tokens=max_new_tokens,\n",
-    "        top_p=top_p,\n",
-    "        repetition_penalty=repetition_penalty,\n",
-    "        do_sample=True,\n",
-    "        seed=42,\n",
-    "    )\n",
-    "\n",
-    "    formatted_prompt = format_prompt(prompt, history)\n",
-    "\n",
-    "    try:\n",
-    "        stream = text_client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)\n",
-    "        output = \"\"\n",
-    "        for response in stream:\n",
-    "            output += response.token.text\n",
-    "            yield output\n",
-    "\n",
-    "    except Exception as e:\n",
-    "         if \"Too Many Requests\" in str(e):\n",
-    "             print(\"ERROR: Too many requests on mistral client\")\n",
-    "             gr.Warning(\"Unfortunately Mistral is unable to process\")\n",
-    "             output = \"Unfortuanately I am not able to process your request now !\"\n",
-    "         else:\n",
-    "             print(\"Unhandled Exception: \", str(e))\n",
-    "             gr.Warning(\"Unfortunately Mistral is unable to process\")\n",
-    "             output = \"I do not know what happened but I could not understand you .\"\n",
-    "             \n",
-    "    return output\n",
-    "\n",
-    "\n",
-    "def transcribe(wav_path):\n",
-    "    \n",
-    "    # get first element from whisper_jax and strip it to delete begin and end space\n",
-    "    return whisper_client.predict(\n",
-    "\t\t\t\twav_path,\t# str (filepath or URL to file) in 'inputs' Audio component\n",
-    "\t\t\t\t\"transcribe\",\t# str in 'Task' Radio component\n",
-    "                False, # return_timestamps=False for whisper-jax https://gist.github.com/sanchit-gandhi/781dd7003c5b201bfe16d28634c8d4cf#file-whisper_jax_endpoint-py\n",
-    "\t\t\t\tapi_name=\"/predict\"\n",
-    "    )[0].strip()\n",
-    "    \n",
-    "\n",
-    "# Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n",
-    "\n",
-    "\n",
-    "def add_text(history, text):\n",
-    "    history = [] if history is None else history\n",
-    "    history = history + [(text, None)]\n",
-    "    return history, gr.update(value=\"\", interactive=False)\n",
-    "\n",
-    "\n",
-    "def add_file(history, file):\n",
-    "    history = [] if history is None else history\n",
-    "    \n",
-    "    try:\n",
-    "        text = transcribe(\n",
-    "            file\n",
-    "        )\n",
-    "        print(\"Transcribed text:\",text)\n",
-    "    except Exception as e:\n",
-    "        print(str(e))\n",
-    "        gr.Warning(\"There was an issue with transcription, please try writing for now\")\n",
-    "        # Apply a null text on error\n",
-    "        text = \"Transcription seems failed, please tell me a joke about chickens\"\n",
-    "    \n",
-    "    history = history + [(text, None)]\n",
-    "    return history\n",
-    "\n",
-    "\n",
-    "\n",
-    "def bot(history, system_prompt=\"\"):    \n",
-    "    history = [] if history is None else history\n",
-    "\n",
-    "    if system_prompt == \"\":\n",
-    "        system_prompt = system_message\n",
-    "        \n",
-    "    history[-1][1] = \"\"\n",
-    "    for character in generate(history[-1][0], history[:-1]):\n",
-    "        history[-1][1] = character\n",
-    "        yield history  \n",
-    "\n",
-    "\n",
-    "def get_latents(speaker_wav):\n",
-    "    # Generate speaker embedding and latents for TTS\n",
-    "    gpt_cond_latent, diffusion_conditioning, speaker_embedding = model.get_conditioning_latents(audio_path=speaker_wav)\n",
-    "    return gpt_cond_latent, diffusion_conditioning, speaker_embedding\n",
-    "\n",
-    "latent_map={}\n",
-    "latent_map[\"Female_Voice\"] = get_latents(\"examples/female.wav\")\n",
-    "\n",
-    "def get_voice(prompt,language, latent_tuple,suffix=\"0\"):\n",
-    "    gpt_cond_latent,diffusion_conditioning, speaker_embedding = latent_tuple\n",
-    "    # Direct version\n",
-    "    t0 = time.time()\n",
-    "    out = model.inference(\n",
-    "        prompt,\n",
-    "        language,\n",
-    "        gpt_cond_latent,\n",
-    "        speaker_embedding,\n",
-    "        diffusion_conditioning\n",
-    "    )\n",
-    "    inference_time = time.time() - t0\n",
-    "    print(f\"I: Time to generate audio: {round(inference_time*1000)} milliseconds\")\n",
-    "    real_time_factor= (time.time() - t0) / out['wav'].shape[-1] * 24000\n",
-    "    print(f\"Real-time factor (RTF): {real_time_factor}\")\n",
-    "    wav_filename=f\"output_{suffix}.wav\"\n",
-    "    torchaudio.save(wav_filename, torch.tensor(out[\"wav\"]).unsqueeze(0), 24000)\n",
-    "    return wav_filename\n",
-    "\n",
-    "def generate_speech(history):\n",
-    "    text_to_generate = history[-1][1]\n",
-    "    text_to_generate = text_to_generate.replace(\"\\n\", \" \").strip()\n",
-    "    text_to_generate = nltk.sent_tokenize(text_to_generate)\n",
-    "\n",
-    "    language = \"en\"\n",
-    "\n",
-    "    wav_list = []\n",
-    "    for i,sentence in enumerate(text_to_generate):\n",
-    "        # Sometimes prompt </s> coming on output remove it \n",
-    "        sentence= sentence.replace(\"</s>\",\"\")\n",
-    "        # A fast fix for last chacter, may produce weird sounds if it is with text\n",
-    "        if sentence[-1] in [\"!\",\"?\",\".\",\",\"]:\n",
-    "            #just add a space\n",
-    "            sentence = sentence[:-1] + \" \" + sentence[-1]\n",
-    "        \n",
-    "        print(\"Sentence:\", sentence)\n",
-    "        \n",
-    "        try:   \n",
-    "            # generate speech using precomputed latents\n",
-    "            # This is not streaming but it will be fast\n",
-    "            \n",
-    "            # giving sentence suffix so we can merge all to single audio at end\n",
-    "            # On mobile there is no autoplay support due to mobile security!\n",
-    "            wav = get_voice(sentence,language, latent_map[\"Female_Voice\"], suffix=i)\n",
-    "            wav_list.append(wav)\n",
-    "            \n",
-    "            yield wav\n",
-    "            wait_time= librosa.get_duration(path=wav)\n",
-    "            print(\"Sleeping till audio end\")\n",
-    "            time.sleep(wait_time)\n",
-    "\n",
-    "        except RuntimeError as e :\n",
-    "            if \"device-side assert\" in str(e):\n",
-    "                # cannot do anything on cuda device side error, need tor estart\n",
-    "                print(f\"Exit due to: Unrecoverable exception caused by prompt:{sentence}\", flush=True)\n",
-    "                gr.Warning(\"Unhandled Exception encounter, please retry in a minute\")\n",
-    "                print(\"Cuda device-assert Runtime encountered need restart\")\n",
-    "\n",
-    "                \n",
-    "                # HF Space specific.. This error is unrecoverable need to restart space \n",
-    "                api.restart_space(repo_id=repo_id)\n",
-    "            else:\n",
-    "                print(\"RuntimeError: non device-side assert error:\", str(e))\n",
-    "                raise e\n",
-    "    #Spoken on autoplay everysencen now produce a concataned one at the one\n",
-    "    #requires pip install ffmpeg-python\n",
-    "    files_to_concat= [ffmpeg.input(w) for w in wav_list]\n",
-    "    combined_file_name=\"combined.wav\"\n",
-    "    ffmpeg.concat(*files_to_concat,v=0, a=1).output(combined_file_name).run(overwrite_output=True)\n",
-    "\n",
-    "    return gr.Audio.update(value=combined_file_name, autoplay=False)\n",
-    "     \n",
-    "\n",
-    "with gr.Blocks(title=title) as demo:\n",
-    "    gr.Markdown(DESCRIPTION)\n",
-    "    \n",
-    "    \n",
-    "    chatbot = gr.Chatbot(\n",
-    "        [],\n",
-    "        elem_id=\"chatbot\",\n",
-    "        avatar_images=('examples/lama.jpeg', 'examples/lama2.jpeg'),\n",
-    "        bubble_full_width=False,\n",
-    "    )\n",
-    "\n",
-    "    with gr.Row():\n",
-    "        txt = gr.Textbox(\n",
-    "            scale=3,\n",
-    "            show_label=False,\n",
-    "            placeholder=\"Enter text and press enter, or speak to your microphone\",\n",
-    "            container=False,\n",
-    "        )\n",
-    "        txt_btn = gr.Button(value=\"Submit text\",scale=1)\n",
-    "        btn = gr.Audio(source=\"microphone\", type=\"filepath\", scale=4)\n",
-    "        \n",
-    "    with gr.Row():\n",
-    "        audio = gr.Audio(type=\"numpy\", streaming=False, autoplay=True, label=\"Generated audio response\", show_label=True)\n",
-    "\n",
-    "    clear_btn = gr.ClearButton([chatbot, audio])\n",
-    "    \n",
-    "    txt_msg = txt_btn.click(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(\n",
-    "        bot, chatbot, chatbot\n",
-    "    ).then(generate_speech, chatbot, audio)\n",
-    "\n",
-    "    txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False)\n",
-    "\n",
-    "    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(\n",
-    "        bot, chatbot, chatbot\n",
-    "    ).then(generate_speech, chatbot, audio)\n",
-    "    \n",
-    "    txt_msg.then(lambda: gr.update(interactive=True), None, [txt], queue=False)\n",
-    "    \n",
-    "    file_msg = btn.stop_recording(add_file, [chatbot, btn], [chatbot], queue=False).then(\n",
-    "        bot, chatbot, chatbot\n",
-    "    ).then(generate_speech, chatbot, audio)\n",
-    "    \n",
-    "\n",
-    "    gr.Markdown(\"\"\"\n",
-    "This Space demonstrates how to speak to a chatbot, based solely on open-source models.\n",
-    "It relies on 3 models:\n",
-    "1. [Whisper-large-v2](https://huggingface.co/spaces/sanchit-gandhi/whisper-jax) as an ASR model, to transcribe recorded audio to text. It is called through a [gradio client](https://www.gradio.app/docs/client).\n",
-    "2. [Mistral-7b-instruct](https://huggingface.co/spaces/osanseviero/mistral-super-fast) as the chat model, the actual chat model. It is called from [huggingface_hub](https://huggingface.co/docs/huggingface_hub/guides/inference).\n",
-    "3. [Coqui's XTTS](https://huggingface.co/spaces/coqui/xtts) as a TTS model, to generate the chatbot answers. This time, the model is hosted locally.\n",
-    "\n",
-    "Note:\n",
-    "- By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml\"\"\")\n",
-    "demo.queue()\n",
-    "demo.launch(debug=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "652d675a-8912-44cb-830d-29fc5d6679d4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
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