File size: 2,158 Bytes
25ae981 | 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 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
from pathlib import Path
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
PROMPT = "Please transcribe this audio."
def build_conversation(audio_path: Path) -> list[dict]:
return [
{
"role": "user",
"content": [
{"type": "audio", "path": str(audio_path)},
{"type": "text", "text": PROMPT},
],
}
]
def main() -> None:
parser = argparse.ArgumentParser(description="Transcribe one audio file with audio8-asr-0.1B.")
parser.add_argument("audio", type=Path)
parser.add_argument("--model", default=".")
parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu")
parser.add_argument("--max_new_tokens", type=int, default=128)
parser.add_argument("--max_audio_seconds", type=int, default=30)
args = parser.parse_args()
device = torch.device(args.device)
dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
processor = AutoProcessor.from_pretrained(args.model, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
args.model,
trust_remote_code=True,
torch_dtype=dtype,
attn_implementation="eager",
).to(device)
model.eval()
batch = processor.apply_chat_template(
build_conversation(args.audio),
return_tensors="pt",
sampling_rate=16000,
audio_padding="longest",
add_generation_prompt=True,
audio_max_length=int(args.max_audio_seconds) * 16000,
text_kwargs={"padding": "longest", "truncation": True, "max_length": 1000},
)
batch = {key: value.to(device) if hasattr(value, "to") else value for key, value in dict(batch).items()}
with torch.inference_mode():
output_ids = model.generate(**batch, max_new_tokens=args.max_new_tokens, do_sample=False)
prompt_len = int(batch["input_ids"].shape[1])
text = processor.decode(output_ids[0, prompt_len:], skip_special_tokens=True).strip()
print(text)
if __name__ == "__main__":
main()
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