Vaibhavs10

from huggingface_hub import HfApi
from huggingface_hub import logging

logging.set_verbosity_info()

api = HfApi()

api.upload_folder(folder_path="<FOLDER NAME>", 
                  repo_id="<DATASET NAME>", 
                  repo_type="dataset", 

Vaibhavs10

import transformers

model_name = 'Intel/neural-chat-7b-v3-1'
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)


def generate_response(system_input, user_input):
  
    # Format the input using the provided template

Vaibhavs10

import json
import time
import torch
from transformers import pipeline

pipe = pipeline(
    "automatic-speech-recognition",
    "openai/whisper-large-v3",
    torch_dtype=torch.float16,
    device="mps",

Vaibhavs10

import json

import argparse
import torch
from transformers import pipeline

parser = argparse.ArgumentParser(description="Automatic Speech Recognition")
parser.add_argument(
    "--file-name",
    required=True,

Vaibhavs10

!pip install transformers optimum
!pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name_or_path = "TheBloke/zephyr-7B-beta-GPTQ"

model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,

Vaibhavs10

# Load the dataset (locally)

from datasets import load_dataset

cv_11 = load_dataset("mozilla-foundation/common_voice_11_0", "hi", split="train")

# Stream the dataset

from datasets import load_dataset

Vaibhavs10

#pip install git+https://github.com/huggingface/transformers.git

import datetime
import sys
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_microphone_live

pipe = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=0)
sampling_rate = pipe.feature_extractor.sampling_rate

Vaibhavs10

user_name=
ssh_key=""

cd /home
sudo useradd -m "$user_name"
sudo mkdir /home/"$user_name"/.ssh
echo "$ssh_key" | sudo tee -a /home/"$user_name"/.ssh/authorized_keys
sudo chsh -s /usr/bin/bash "$user_name"
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs

Vaibhavs10

Hey hey!

We are on a mission to democratise speech, increase the language coverage of current SoTA speech recognition and push the limits of what is possible. Come join us from December 5th - 19th for a community sprint powered by Lambda Labs. Through this sprint, we'll cover 70+ languages, 39M - 1550M parameters & evaluate our models on real-world evaluation datasets.

Register your interest via the Google form here.

What is the sprint about ❓

The goal of the sprint is to fine-tune Whisper in as many languages as possible and make them accessible to the community. We hope that especially low-resource languages will profit from this event.

Vaibhavs10

Motivation

Whilst the current ASR landscape is really promosing a lot of it is currently benchmarked on rather "clean" datasets. This often creates a false sense of confidence in the Architecture which might not translate to the real world.

Types of Noises

1. Gaussian White Noise
2. Real World Noise
3. Choppy audio (random 1-2s removed from the audio snippet)
4. Speed up (random 10s snippets sped up than the rest)

Evaluation