Correct readme

README.md

@@ -26,7 +26,6 @@ tags:
 - speech
 - Whisper
 ---
-
 # CarelessWhisper - Causal Whisper Streaming Model
 Causal Whisper Streaming is a fine tuned version of OpenAI Whisper, which can handle causal data and perform real-time transcription. 
 
@@ -34,11 +33,36 @@ Causal Whisper Streaming is a fine tuned version of OpenAI Whisper, which can ha
 [![Demo on Hugging Face](https://img.shields.io/badge/🤗%20Demo-Hugging%20Face-blueviolet?logo=huggingface&logoColor=white)](https://huggingface.co/spaces/MLSpeech/CarelessWhisper-causal-streaming)
 
 
-## Setup
+## 🔧 Setup
 We used Python 3.9.16, PyTorch 2.6.0, and PyTorch-Lightning 2.5.0 to train and test our models.
-All of the required dependencies are available on `requirements.txt` file. Make sure all of the packages are installed before running anything.
 Portions of this code are adapted from [OpenAI's Whisper](https://github.com/openai/whisper).
 
+To set up the project environment using `conda`, follow these steps:
+
+1. **Clone the repository**  
+   ```bash
+   git clone https://github.com/tomer9080/CarelessWhisper-streaming
+   cd CarelessWhisper-streaming
+   ```
+
+> 💡 Make sure you have [Miniconda](https://docs.conda.io/en/latest/miniconda.html) or [Anaconda](https://www.anaconda.com/products/distribution) installed before proceeding.
+
+2. **Create the conda environment**
+    ```bash
+    conda env create -f environment.yml
+    ```
+
+3. **Activate The environment**
+    ```bash
+    conda activate careless_whisper
+    ```
+
+4. **Install the appropriate PyTorch version**  
+   Depending on your hardware and CUDA version, install PyTorch by following the instructions at [https://pytorch.org/get-started/locally](https://pytorch.org/get-started/locally).  
+   This project was tested with CUDA 12.4, but it should also work with compatible earlier or later versions.
+ 
+After installing all of the dependencies, you can try to run inference.
+
 ## Available Models
 We fine-tuned three different sizes of Whisper, all support english only transcription.
 A `large-v2` that was fine tuned on multilingual data is available, and supports English, French, Spanish, German and Portuguese with chunk size of 300 miliseconds.
@@ -51,33 +75,57 @@ A `large-v2` that was fine tuned on multilingual data is available, and supports
 
 
 ## Running Inference
-To run inference, download the repo content, and run accroding to following sections. 
+To run inference, download the repo content, and run from the repository root accroding to following sections.
+
+> **Note:** The models are hosted on the [Hugging Face Hub](https://huggingface.co/), which requires an access token.  
+> Make sure you are logged in with your token to access the models.
+
+### How to Apply Your Hugging Face Access Token
+
+1. **Create a Hugging Face account** (if you don’t have one) at [https://huggingface.co/join](https://huggingface.co/join).
+
+2. **Generate an access token:**
+   - Go to your Hugging Face account settings: [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens)
+   - Click on **"New token"**, give it a name, select the appropriate scopes (usually `read` is enough), and create it.
+
+3. **Login using the Hugging Face CLI:**  
+   Install the CLI if you don’t have it:
+   ```bash
+   pip install huggingface_hub
+   ```
+   Then login:
+   ```bash
+   huggingface-cli login
+   ```
+   Paste your token when prompted.
+
+
 ### CLI Usage
 The transcription model is easily activated using the next command:
 ```bash
 # Using a local microphone for streaming transcription, dumping the recording to out.wav
-python causal_whisper_stream/streaming_transcribe.py \
---output_filename out.wav \  # path to dump recording to - optional
---channels 2 \  # number of channels
---model small \   # model size
---chunk_size 300 \  # chunk size in milliseconds
---device cuda \  # inference device
---beam_size 5 \  # run inference with beam of size 5 
---ca_kv_cache \  # run inference with CA kv cache
+python transcribe.py \
+--output_filename out.wav \
+--channels 2 \
+--model small \ 
+--chunk_size 300 \
+--device cuda \
+--beam_size 5 \
+--ca_kv_cache \
 ```
 
 A simulation of a stream on a wav file is also available:
 ```bash
 # Simulating a stream on a wav file
-python causal_whisper_stream/streaming_transcribe.py \
---model small \   # model size
---chunk_size 300 \  # chunk size in milliseconds
---device cuda \  # inference device
---beam_size 5 \  # run inference with beam of size 5 
---ca_kv_cache \  # run inference with CA kv cache
---wav_file /path/to/audio.wav \  # path to audio file to transcribe
---simulate_stream \  # a must for a simulation to occur
---use_latency \  # simulating real latency of the chunk size - optional.
+python transcribe.py \
+--model small \
+--chunk_size 300 \
+--device cuda \
+--beam_size 5 \
+--ca_kv_cache \
+--wav_file /path/to/audio.wav \
+--simulate_stream \
+--use_latency
 ```
 
 ### Python Usage
@@ -85,14 +133,14 @@ If you prefer using python, a code sinppet utilizing a microphone or a wav file
 
 ```python
 import torch
-import causal_whisper_stream
+import careless_whisper_stream
 
 model_size = "small" # model size
 chunk_size = 300 # chunk size in milliseconds
 multilingual = False # currently on large-v2_300msec supports other languages than english.
-device = "cuda" if torch.cuda.is_avaialable() else "cpu"
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-model = causal_whisper_stream.load_streaming_model(name=model_size,
+model = careless_whisper_stream.load_streaming_model(name=model_size,
                                                    gran=chunk_size,
                                                    multilingual=multilingual,
                                                    device=device)
@@ -113,31 +161,46 @@ texts_wav_simulation = model.transcribe(simulate_stream=True,
 ## Training
 In order to train using LoRA, you can use our existing code. Make sure all the requirements are installed. 
 
+### Dataset Structure
+
+Before starting model training using the command-line interface provided below, you must first configure your dataset dictionary file located at `training_code/ds_dict.py`.
+
+This file defines a Python dictionary named `ds_paths`, where you should specify paths to the `train`, `val`, and `test` partitions of your dataset. Each partition should be a CSV file with the following three columns:
+
+1. `wav_path` — Path to the WAV audio file.  
+2. `tg_path` — Path to the corresponding `.TextGrid` file containing forced alignment.  
+3. `raw_text` — Ground truth transcription.
+
+> **Note:** The dictionary key (i.e., the name of the dataset) will be used by the training script to identify and load the dataset correctly.
+
+You can find an example entry in `training_code/ds_dict.py`.
+
 ### CLI Interface
 ```bash
-# 
 python training_code/train.py \
---lora \  # use LoRA fine-tuning, currently only this way is supported.
---streaming_train \  # streaming traing
---simulate_stream \  # simulate a stream of data, use streaming mel
---dataset LIBRI-960-ALIGNED \  # dataset name from a dict 
---name example_training_base_model \  # name for differentiation
---size base \  # model size to train on 
---batch_size 32 \  # batch size
---epochs 10 \  # maximal epochs
---learning_rate 1e-5 \  # initial learning rate
---rank 32 \  # LoRA rank 
---gran 15 \  # Chunk size in encoder frames units. 15 encoder frames = 300msec
---extra_gran_blocks 1 \  # extra blocks for inital chunk. 1 means that an extra block is added for the beginning. i.e., the inital chunk will be 600msec.
---streaming_fraction 0.25 \  # fraction of samples out of the possible subsequences in the streaming process to train on
---top_k 5 \  # how many ckpts to save.
+--lora \
+--streaming_train \
+--simulate_stream \
+--dataset LIBRI-960-ALIGNED \
+--name example_training_base_model \
+--size base \
+--batch_size 32 \
+--epochs 10 \
+--learning_rate 1e-5 \
+--rank 32 \
+--gran 15 \
+--extra_gran_blocks 1 \
+--streaming_fraction 0.25 \
+--top_k 5 \
 ```
 
 For more options and training configurations, run:
 ```bash
 python training_code/train.py --help
 ```
-### 🙏 Acknowledgements
+## 🙏 Acknowledgements
 
 This project uses components from [OpenAI's Whisper](https://github.com/openai/whisper), licensed under the MIT License.
 
+
+