Update README.md

README.md

-# whisper-webservice
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+# Whisper Webservice
+
+The webservice will be available soon.
+
+Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.
+
+## Docker Setup
+
+The docker image will be available soon
+
+## Setup
+
+We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. The following command will pull and install the latest commit from this repository, along with its Python dependencies 
+
+    pip install git+https://github.com/openai/whisper.git 
+
+It also requires the command-line tool [`ffmpeg`](https://ffmpeg.org/) to be installed on your system, which is available from most package managers:
+
+```bash
+# on Ubuntu or Debian
+sudo apt update && sudo apt install ffmpeg
+
+# on MacOS using Homebrew (https://brew.sh/)
+brew install ffmpeg                         
+
+# on Windows using Chocolatey (https://chocolatey.org/)
+choco install ffmpeg
+```
+
+## Command-line usage
+
+The following command will transcribe speech in audio files, using the `medium` model:
+
+    whisper audio.flac audio.mp3 audio.wav --model medium
+
+The default setting (which selects the `small` model) works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the `--language` option:
+
+    whisper japanese.wav --language Japanese
+
+Adding `--task translate` will translate the speech into English:
+
+    whisper japanese.wav --language Japanese --task translate
+
+Run the following to view all available options:
+
+    whisper --help
+
+See [tokenizer.py](whisper/tokenizer.py) for the list of all available languages.
+
+
+## Python usage
+
+Transcription can also be performed within Python: 
+
+```python
+import whisper
+
+model = whisper.load_model("base")
+result = model.transcribe("audio.mp3")
+print(result["text"])
+```
+
+Internally, the `transcribe()` method reads the entire file and processes the audio with a sliding 30-second window, performing autoregressive sequence-to-sequence predictions on each window.
+
+Below is an example usage of `whisper.detect_language()` and `whisper.decode()` which provide lower-level access to the model.
+
+```python
+import whisper
+
+model = whisper.load_model("base")
+
+# load audio and pad/trim it to fit 30 seconds
+audio = whisper.load_audio("audio.mp3")
+audio = whisper.pad_or_trim(audio)
+
+# make log-Mel spectrogram and move to the same device as the model
+mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+# detect the spoken language
+_, probs = model.detect_language(mel)
+print(f"Detected language: {max(probs, key=probs.get)}")
+
+# decode the audio
+options = whisper.DecodingOptions()
+result = whisper.decode(model, mel, options)
+
+# print the recognized text
+print(result.text)
+```
+
+## License
+
+The code and the model weights of Whisper are released under the MIT License. See [LICENSE](LICENSE) for further details.