https://github.com/kadirnar/whisper-plus

WhisperPlus: Advancing Speech-to-Text Processing 🚀
https://github.com/kadirnar/whisper-plus

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WhisperPlus: Advancing Speech-to-Text Processing 🚀

• Host: GitHub
• URL: https://github.com/kadirnar/whisper-plus
• Owner: kadirnar
• License: apache-2.0
• Created: 2023-11-21T09:59:50.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-04-29T19:23:16.000Z (6 months ago)
• Last Synced: 2024-05-01T16:28:37.042Z (6 months ago)
• Language: Python
• Homepage:
• Size: 184 KB
• Stars: 1,318
• Watchers: 16
• Forks: 107
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • Funding: .github/FUNDING.yml
  • License: LICENSE

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• StarryDivineSky - kadirnar/whisper-plus

README

        




    WhisperPlus: Faster, Smarter, and More Capable 🚀





    





    

        

    

    

    





## 🛠️ Installation

```bash

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

pip install flash-attn --no-build-isolation

```

## 🤗 Model Hub

You can find the models on the [HuggingFace Model Hub](https://huggingface.co/models?search=whisper)

## 🎙️ Usage

To use the whisperplus library, follow the steps below for different tasks:

### 🎵 Youtube URL to Audio

```python

from whisperplus import SpeechToTextPipeline, download_youtube_to_mp3

from transformers import BitsAndBytesConfig, HqqConfig

import torch

url = "https://www.youtube.com/watch?v=di3rHkEZuUw"

audio_path = download_youtube_to_mp3(url, output_dir="downloads", filename="test")

hqq_config = HqqConfig(

    nbits=4,

    group_size=64,

    quant_zero=False,

    quant_scale=False,

    axis=0,

    offload_meta=False,

)  # axis=0 is used by default

bnb_config = BitsAndBytesConfig(

    load_in_4bit=True,

    bnb_4bit_quant_type="nf4",

    bnb_4bit_compute_dtype=torch.bfloat16,

    bnb_4bit_use_double_quant=True,

)

pipeline = SpeechToTextPipeline(

    model_id="distil-whisper/distil-large-v3",

    quant_config=hqq_config,

    flash_attention_2=True,

)

transcript = pipeline(

    audio_path=audio_path,

    chunk_length_s=30,

    stride_length_s=5,

    max_new_tokens=128,

    batch_size=100,

    language="english",

    return_timestamps=False,

)

print(transcript)

```

### 🍎 Apple MLX

```python

from whisperplus.pipelines import mlx_whisper

from whisperplus import download_youtube_to_mp3

url = "https://www.youtube.com/watch?v=1__CAdTJ5JU"

audio_path = download_youtube_to_mp3(url)

text = mlx_whisper.transcribe(

    audio_path, path_or_hf_repo="mlx-community/whisper-large-v3-mlx"

)["text"]

print(text)

```

### 🍏 Lightning Mlx Whisper

```python

from whisperplus.pipelines.lightning_whisper_mlx import LightningWhisperMLX

from whisperplus import download_youtube_to_mp3

url = "https://www.youtube.com/watch?v=1__CAdTJ5JU"

audio_path = download_youtube_to_mp3(url)

whisper = LightningWhisperMLX(model="distil-large-v3", batch_size=12, quant=None)

output = whisper.transcribe(audio_path=audio_path)["text"]

```

### 📰 Summarization

```python

from whisperplus.pipelines.summarization import TextSummarizationPipeline

summarizer = TextSummarizationPipeline(model_id="facebook/bart-large-cnn")

summary = summarizer.summarize(transcript)

print(summary[0]["summary_text"])

```

### 📰 Long Text Support Summarization

```python

from whisperplus.pipelines.long_text_summarization import LongTextSummarizationPipeline

summarizer = LongTextSummarizationPipeline(model_id="facebook/bart-large-cnn")

summary_text = summarizer.summarize(transcript)

print(summary_text)

```

### 💬 Speaker Diarization

You must confirm the licensing permissions of these two models.

- https://huggingface.co/pyannote/speaker-diarization-3.1

- https://huggingface.co/pyannote/segmentation-3.0

```bash

pip install -r requirements/speaker_diarization.txt

pip install -U "huggingface_hub[cli]"

huggingface-cli login

```

```python

from whisperplus.pipelines.whisper_diarize import ASRDiarizationPipeline

from whisperplus import download_youtube_to_mp3, format_speech_to_dialogue

audio_path = download_youtube_to_mp3("https://www.youtube.com/watch?v=mRB14sFHw2E")

device = "cuda"  # cpu or mps

pipeline = ASRDiarizationPipeline.from_pretrained(

    asr_model="openai/whisper-large-v3",

    diarizer_model="pyannote/speaker-diarization-3.1",

    use_auth_token=False,

    chunk_length_s=30,

    device=device,

)

output_text = pipeline(audio_path, num_speakers=2, min_speaker=1, max_speaker=2)

dialogue = format_speech_to_dialogue(output_text)

print(dialogue)

```

### ⭐ RAG - Chat with Video(LanceDB)

```bash

pip install sentence-transformers ctransformers langchain

```

```python

from whisperplus.pipelines.chatbot import ChatWithVideo

chat = ChatWithVideo(

    input_file="trascript.txt",

    llm_model_name="TheBloke/Mistral-7B-v0.1-GGUF",

    llm_model_file="mistral-7b-v0.1.Q4_K_M.gguf",

    llm_model_type="mistral",

    embedding_model_name="sentence-transformers/all-MiniLM-L6-v2",

)

query = "what is this video about ?"

response = chat.run_query(query)

print(response)

```

### 🌠 RAG - Chat with Video(AutoLLM)

```bash

pip install autollm>=0.1.9

```

```python

from whisperplus.pipelines.autollm_chatbot import AutoLLMChatWithVideo

# service_context_params

system_prompt = """

You are an friendly ai assistant that help users find the most relevant and accurate answers

to their questions based on the documents you have access to.

When answering the questions, mostly rely on the info in documents.

"""

query_wrapper_prompt = """

The document information is below.

---------------------

{context_str}

---------------------

Using the document information and mostly relying on it,

answer the query.

Query: {query_str}

Answer:

"""

chat = AutoLLMChatWithVideo(

    input_file="input_dir",  # path of mp3 file

    openai_key="YOUR_OPENAI_KEY",  # optional

    huggingface_key="YOUR_HUGGINGFACE_KEY",  # optional

    llm_model="gpt-3.5-turbo",

    llm_max_tokens="256",

    llm_temperature="0.1",

    system_prompt=system_prompt,

    query_wrapper_prompt=query_wrapper_prompt,

    embed_model="huggingface/BAAI/bge-large-zh",  # "text-embedding-ada-002"

)

query = "what is this video about ?"

response = chat.run_query(query)

print(response)

```

### 🎙️ Text to Speech

```python

from whisperplus.pipelines.text2speech import TextToSpeechPipeline

tts = TextToSpeechPipeline(model_id="suno/bark")

audio = tts(text="Hello World", voice_preset="v2/en_speaker_6")

```

### 🎥 AutoCaption

```bash

pip install moviepy

apt install imagemagick libmagick++-dev

cat /etc/ImageMagick-6/policy.xml | sed 's/none/read,write/g'> /etc/ImageMagick-6/policy.xml

```

```python

from whisperplus.pipelines.whisper_autocaption import WhisperAutoCaptionPipeline

from whisperplus import download_youtube_to_mp4

video_path = download_youtube_to_mp4(

    "https://www.youtube.com/watch?v=di3rHkEZuUw",

    output_dir="downloads",

    filename="test",

)  # Optional

caption = WhisperAutoCaptionPipeline(model_id="openai/whisper-large-v3")

caption(video_path=video_path, output_path="output.mp4", language="english")

```

## 😍 Contributing

```bash

pip install pre-commit

pre-commit install

pre-commit run --all-files

```

## 📜 License

This project is licensed under the terms of the Apache License 2.0.

## 🤗 Citation

```bibtex

@misc{radford2022whisper,

  doi = {10.48550/ARXIV.2212.04356},

  url = {https://arxiv.org/abs/2212.04356},

  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},

  title = {Robust Speech Recognition via Large-Scale Weak Supervision},

  publisher = {arXiv},

  year = {2022},

  copyright = {arXiv.org perpetual, non-exclusive license}

}

```