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{
"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)"
]
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|