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import torch
from transformers import WhisperForConditionalGeneration, WhisperProcessor
from transformers.models.whisper.tokenization_whisper import LANGUAGES
from transformers.pipelines.audio_utils import ffmpeg_read
import librosa
import gradio as gr
model_id = "openai/whisper-large-v2"
processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)
sampling_rate = processor.feature_extractor.sampling_rate
bos_token_id = processor.tokenizer.all_special_ids[-106]
decoder_input_ids = torch.tensor([bos_token_id])
def process_audio_file(file):
with open(file, "rb") as f:
inputs = f.read()
audio = ffmpeg_read(inputs, sampling_rate)
return audio
def transcribe(Microphone, File_Upload):
warn_output = ""
if (Microphone is not None) and (File_Upload is not None):
warn_output = "WARNING: You've uploaded an audio file and used the microphone. " \
"The recorded file from the microphone will be used and the uploaded audio will be discarded.\n"
file = Microphone
elif (Microphone is None) and (File_Upload is None):
return "ERROR: You have to either use the microphone or upload an audio file"
elif Microphone is not None:
file = Microphone
else:
file = File_Upload
audio_data = process_audio_file(file)
input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
with torch.no_grad():
logits = model.forward(input_features, decoder_input_ids=decoder_input_ids).logits
pred_ids = torch.argmax(logits, dim=-1)
lang_ids = processor.decode(pred_ids[0])
lang_ids = lang_ids.lstrip("<|").rstrip("|>")
language = LANGUAGES[lang_ids]
return language
iface = gr.Interface(
fn=transcribe,
inputs=[
gr.inputs.Audio(source="microphone", type='filepath', optional=True),
gr.inputs.Audio(source="upload", type='filepath', optional=True),
],
outputs="text",
layout="horizontal",
theme="huggingface",
title="Whisper Language Identification",
description="Demo for Language Identification using OpenAI's [Whisper Large V2](https://huggingface.co/openai/whisper-large-v2)",
allow_flagging='never',
)
iface.launch(enable_queue=True) |