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Dockerfile

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+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+ PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+from fastapi import FastAPI, UploadFile
+from moviepy.editor import *
+from transformers import AutoTokenizer , AutoModelForSeq2SeqLM , pipeline
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+from transformers import WhisperFeatureExtractor, WhisperTokenizer
+import librosa
+import numpy as np
+import torch 
+
+
+
+app = FastAPI()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device  
+
+feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
+tokenizer = WhisperTokenizer.from_pretrained("openai/whisper-small", language="Swahili", task="transcribe")
+processor = WhisperProcessor.from_pretrained("Jayem-11/whisper-small-swahili-3")
+asr_model = WhisperForConditionalGeneration.from_pretrained('Jayem-11/whisper-small-swahili-3')
+forced_decoder_ids = processor.get_decoder_prompt_ids(language="sw", task="transcribe")
+
+
+@app.get("/")
+async def read_root():
+    return {"Successful"}
+
+
+def extract_and_resample_audio(file):
+
+    with open('audio.wav', 'wb') as f:
+    
+        f.write(file)   
+
+    # Load the temporary audio file
+    audio_data, sr = librosa.load("audio.wav")
+
+    # Resample the audio to 16000Hz
+    audio_resampled = librosa.resample(audio_data, orig_sr = sr, target_sr=16000)
+    
+    print("Done resampling")
+
+    return audio_resampled
+
+@app.post("/predict")
+async def predict(file: UploadFile):
+    audio_resampled = extract_and_resample_audio(await file.read())
+
+
+    input_feats = feature_extractor(audio_resampled, sampling_rate = 16000).input_features[0]
+
+
+    input_feats = np.expand_dims(input_feats, axis=0)
+
+
+    input_feats = torch.from_numpy(input_feats)
+
+
+    output = asr_model.generate(input_features=input_feats.to(device),max_new_tokens=255,).cpu().numpy()
+
+
+    sample_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+
+    return {'Text': sample_text}

requirements.txt

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+fastapi
+uvicorn
+transformers
+moviepy
+librosa
+numpy
+torch
+python-multipart
+sentencepiece 
+protobuf