wehub-resource-sync
83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
from ray import serve
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import re
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import subprocess
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from typing import List
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import starlette.requests
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def hard_normalize(word):
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"""Lower case the word and remove all non-alpha-numeric characters
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from the entire word.
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"""
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non_alpha_numeric = re.compile(r"[\W]+")
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return non_alpha_numeric.sub("", word.lower())
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def clean_whisper_alignments(whisper_word_alignments: List[dict]) -> List[dict]:
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"""Change required to match gentle's tokenization with Whisper's word alignments"""
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processed_words = []
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for word_alignment in whisper_word_alignments:
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if word_alignment.word == "%":
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processed_words.append(word_alignment._replace(word=" percent"))
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elif word_alignment.word[0] == "'" and len(processed_words) > 0:
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# eg: "'Or" from ["d", "'Or"]
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processed_words[-1]._replace(
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word=processed_words[-1].word + word_alignment.word,
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end=word_alignment.end,
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)
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elif hard_normalize(word_alignment.word) == "":
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# eg: " -"
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continue
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else:
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processed_words.append(word_alignment)
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return processed_words
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@serve.deployment(ray_actor_options={"num_cpus": 1.0, "num_gpus": 1})
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class WhisperModel:
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def __init__(self, model_size="large-v2"):
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# Load model
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from faster_whisper import WhisperModel
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# Run on GPU with FP16
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self.model = WhisperModel(model_size, device="cuda", compute_type="float16")
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async def transcribe(self, file_path: str):
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subprocess.check_call(["curl", "-o", "audio.mp3", "-sSfLO", file_path])
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segments, info = self.model.transcribe(
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"audio.mp3",
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language="en",
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initial_prompt="Here is the um, uh, Um, Uh, transcript.",
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best_of=5,
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beam_size=5,
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word_timestamps=True,
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)
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whisper_alignments = []
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transcript_text = ""
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for seg in segments:
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transcript_text += seg.text
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whisper_alignments += clean_whisper_alignments(seg.words)
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# Transcript change required to match gentle's tokenization with
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# Whisper's word alignments
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transcript_text = transcript_text.replace("% ", " percent ")
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return {
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"language": info.language,
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"language_probability": info.language_probability,
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"duration": info.duration,
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"transcript_text": transcript_text,
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"whisper_alignments": whisper_alignments,
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}
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async def __call__(self, req: starlette.requests.Request):
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request = await req.json()
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return await self.transcribe(file_path=request["filepath"])
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entrypoint = WhisperModel.bind()
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