Add application file

Dockerfile

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+# Use an official Python image
+FROM python:3.9
+
+# Set the working directory
+WORKDIR /app
+
+# Copy all files from the repo to the container
+COPY . .
+
+# Install dependencies
+RUN pip install --no-cache-dir fastapi uvicorn torch torchaudio transformers
+
+# Expose FastAPI's default port
+EXPOSE 8000
+
+# Start the FastAPI server
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

app.py

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+import torch
+import torchaudio
+from fastapi import FastAPI, UploadFile, File
+from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
+import io
+
+app = FastAPI()
+
+# Load Wav2Vec2 model and processor
+processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft")
+
+
+@app.post("/transcribe/")
+async def transcribe_audio(file: UploadFile = File(...)):
+    try:
+        # Load audio file
+        audio_bytes = await file.read()
+        audio_input, sample_rate = torchaudio.load(io.BytesIO(audio_bytes))
+
+        # Convert stereo to mono (if needed)
+        if audio_input.shape[0] > 1:
+            audio_input = torch.mean(audio_input, dim=0, keepdim=True)
+
+        # Resample to 16 kHz (if needed)
+        target_sample_rate = 16000
+        if sample_rate != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=target_sample_rate)
+            audio_input = resampler(audio_input)
+
+        # Remove batch dimension
+        audio_input = audio_input.squeeze(0)
+
+        # Preprocess the audio
+        input_values = processor(audio_input, sampling_rate=target_sample_rate, return_tensors="pt").input_values
+
+        # Run inference
+        with torch.no_grad():
+            logits = model(input_values).logits
+
+        # Decode the predicted tokens
+        predicted_ids = torch.argmax(logits, dim=-1)
+        transcription = processor.batch_decode(predicted_ids)
+
+        return {"transcription": transcription[0]}
+
+    except Exception as e:
+        return {"error": str(e)}
+

requirements.txt

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+fastapi
+uvicorn[standard]
+phonemizer
+torch
+transformers
+torchaudio
+datasets
+transformers==4.4.0