Backend import for 'as-a-module' use #10
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whisper_online.py
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| self.commited_in_buffer = [] | ||
| self.buffer = [] | ||
| self.new = [] | ||
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| self.last_commited_time = 0 | ||
| self.last_commited_word = None | ||
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| self.output = output |
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Let's rename self.output to self.logfile. Otherwise I agree, good idea.
Btw., logging module should be used, but I am too lazy for that. The Python code should stay simple.
whisper_online.py
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| @@ -465,11 +476,11 @@ def split(self, text): | |||
| #asr = WhisperASR(lan=language, modelsize=size) | |||
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| if args.backend == "faster-whisper": | |||
| from faster_whisper import WhisperModel | |||
| #from faster_whisper import WhisperModel | |||
whisper_online.py
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| asr_cls = FasterWhisperASR | ||
| else: | ||
| import whisper | ||
| import whisper_timestamped | ||
| #import whisper |
whisper_online.py
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| import whisper | ||
| import whisper_timestamped | ||
| #import whisper | ||
| #import whisper_timestamped |
| # join transcribe words with this character (" " for whisper_timestamped, "" for faster-whisper because it emits the spaces when neeeded) | ||
| sep = " " | ||
| sep = " " # join transcribe words with this character (" " for whisper_timestamped, | ||
| # "" for faster-whisper because it emits the spaces when neeeded) |
whisper_online.py
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| def __init__(self, lan, modelsize=None, cache_dir=None, model_dir=None): | ||
| self.transcribe_kargs = {} | ||
| self.original_language = lan | ||
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| self.import_backend() |
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too complicated code. Let's just do the imports in load_model in the childs that need it.
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@Luca-Pozzi Great work! Do you plan on finishing this PR? I would really like to use whisper_online as a module, being unable to greatly limits composability into more end user facing apps. |
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@lrq3000 It is really a minor contribution, but thank you! |
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@Luca-Pozzi Awesome, thank you very much! Please let me know if I can help, I am experienced in Python coding but not in deep learning modules. |
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@Gldkslfmsd I have edited the PR as per your suggestions! |
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Thanks! No worries for late. |
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Thank you both for your great work on this! :D |
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How to use this to allow multiple clients to connect when you host a server or create an API for live transcription? |
I don't know, it's a topic that requires a separate issue. |
Thank you. Using one GPU for each client is a tall ask for me as there could be up to a dozen clients active at a particular time for my use case. I think there are a few backends which do support batched processing. e.g. https://github.com/Blair-Johnson/batch-whisper |
With reference to issue #9, the present PR implements a
import_backedmethod inASRBase. The method is then appropriately overridden both inWhisperTimestampedASRandFasterWhisperASRto import the required libraries.To make the use more flexible, I have also exposed the
outputarg inOnlineASRProcessor. It receives a file-like object, so one could pass:sys.stderr(as before) which in most cases will result into printing to terminalopen("/path/to/file.txt", "a")) to log the output to a text fileopen(os.devnull, "w"), to send the output to/dev/null, i.e. to discard it