NervProj: Setting up support for OpenAI whisper
- We start with the github repository: git@github.com:roche-emmanuel/whisper.git
- We need to prepare a python environment for whisper:
whisper_env: inherit: default_env packages: - numba - numpy - torch - tqdm - more-itertools - tiktoken==0.3.3
- Then we prepare the environment:
nvp pyenv setup whisper_env
- β So far so good π!
- Hmm, actually we probably don't even need this and we can simply use the package openai-whisper instead.
- Just written the whisper_gen component to handle the convertion:
class WhisperGen(NVPComponent): """WhisperGen component class""" def __init__(self, ctx: NVPContext): """Component constructor""" NVPComponent.__init__(self, ctx) self.config = ctx.get_config()["movie_handler"] def process_cmd_path(self, cmd): """Re-implementation of process_cmd_path""" if cmd == "convert": file = self.get_param("input_file") model = self.get_param("model") return self.translate_audio(file, model) return False def translate_audio(self, file, model): """Translate an audio file to text""" logger.info("Should translate audio file to text: %s", file) tools: ToolsManager = self.get_component("tools") ffmpeg_path = tools.get_tool_path("ffmpeg") ffmpeg_dir = self.get_parent_folder(ffmpeg_path) # sys.path.append(ffmpeg_dir) logger.info("Adding path to ffmpeg: %s", ffmpeg_dir) self.append_env_list([ffmpeg_dir], os.environ) # Check that cuda is available: self.check(torch.cuda.is_available(), "Torch CUDA backend is not available ?") model = whisper.load_model(model) result = model.transcribe(file) txt = result["text"] self.write_text_file(txt, file + ".txt") logger.info("Done") logger.info("Generated output: %s", txt) return True
- β And this works just fine!
- Note: models are saved in C:\Users\ultim\.cache\whisper β will make a copy of those just in case .
- Updating python env to get CUDA support:
whisper_env: inherit: default_env packages: - openai-whisper - --extra-index-url https://download.pytorch.org/whl/cu117 - torch - torchvision - torchaudio
- When converting the audio on the CPU we get for instance the duration:
INFO: Done converting auto to text in 161.48 secs
- Performing the same convertion on the GPU we now get:
INFO: Done converting auto to text in 33.66 secs
- The command to transcribe a recording is thus:
nvp audio2text -i nervproj_0006_whisper_integration.x265.mkv
This also works with video files as input !