app.py

import tensorflow as tf
from transformers import Speech2TextProcessor, TFSpeech2TextForConditionalGeneration
from datasets import load_dataset
import soundfile as sf

model = TFSpeech2TextForConditionalGeneration.from_pretrained(
    "facebook/s2t-small-librispeech-asr", from_pt=True
)
processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")


def map_to_array(batch):
    speech, _ = sf.read(batch["file"])
    batch["speech"] = speech
    return batch


ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
ds = ds.map(map_to_array)
ds.set_format(type="tf")

input_features = processor(
    ds["speech"][0], sampling_rate=16000, return_tensors="tf"
).input_features  # Batch size 1
generated_ids = model.generate(input_features)

transcription = processor.batch_decode(generated_ids)