File size: 2,789 Bytes
dde51bf 0196a98 dde51bf b9710dc dde51bf a8d0349 dde51bf a8d0349 dde51bf 8a4eeb2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import torch
from transformers import pipeline
from transformers.pipelines.audio_utils import ffmpeg_read
import gradio as gr
MODEL_NAME = "EwoutLagendijk/whisper-small-indonesian"
BATCH_SIZE = 8
device = 0 if torch.cuda.is_available() else "cpu"
pipe = pipeline(
task="automatic-speech-recognition",
model=MODEL_NAME,
chunk_length_s=30,
device=device,
)
# Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50
def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."):
if seconds is not None:
milliseconds = round(seconds * 1000.0)
hours = milliseconds // 3_600_000
milliseconds -= hours * 3_600_000
minutes = milliseconds // 60_000
milliseconds -= minutes * 60_000
seconds = milliseconds // 1_000
milliseconds -= seconds * 1_000
hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else ""
return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}"
else:
# we have a malformed timestamp so just return it as is
return seconds
def transcribe_speech(filepath):
# Load the audio
audio, sampling_rate = librosa.load(filepath, sr=16000)
# Define chunk size (e.g., 30 seconds)
chunk_duration = 30 # in seconds
chunk_samples = chunk_duration * sampling_rate
# Process audio in chunks
transcription = []
for i in range(0, len(audio), chunk_samples):
chunk = audio[i:i + chunk_samples]
# Convert the chunk into input features
inputs = processor(audio=chunk, sampling_rate=16000, return_tensors="pt").input_features
# Generate transcription for the chunk
generated_ids = model.generate(
inputs,
max_new_tokens=444, # Max allowed by Whisper
forced_decoder_ids=processor.get_decoder_prompt_ids(language="id", task="transcribe")
)
# Decode and append the transcription
chunk_transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
transcription.append(chunk_transcription)
# Combine all chunk transcriptions into a single string
return " ".join(transcription)
demo = gr.Blocks()
mic_transcribe = gr.Interface(
fn=transcribe_speech,
inputs=gr.Audio(sources="microphone", type="filepath"),
outputs=gr.components.Textbox(),
)
file_transcribe = gr.Interface(
fn=transcribe_speech,
inputs=gr.Audio(sources="upload", type="filepath"),
outputs=gr.components.Textbox(),
)
with demo:
gr.TabbedInterface([mic_transcribe, file_transcribe], ["Transcribe Microphone", "Transcribe Audio File"])
demo.launch(share=True, debug=True) |