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from typing import Dict
from transformers.pipelines.audio_utils import ffmpeg_read
import whisper
import torch
SAMPLE_RATE = 16000
MODEL_NAME = "openai/whisper-large" #this always needs to stay in line 8 :D sorry for the hackiness
lang = "dk"
class EndpointHandler():
def __init__(self, path=""):
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
# load the model
#self.model = whisper.load_model("large")
self.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language=lang, task="transcribe")
def __call__(self, data: Dict[str, bytes]) -> Dict[str, str]:
"""
Args:
data (:obj:):
includes the deserialized audio file as bytes
Return:
A :obj:`dict`:. base64 encoded image
"""
# process input
inputs = data.pop("inputs", data)
audio_nparray = ffmpeg_read(inputs, SAMPLE_RATE)
audio_tensor= torch.from_numpy(audio_nparray)
# run inference pipeline
result = self.model.transcribe(audio_nparray)
# postprocess the prediction
return {"tekst": result["text"]} |