First go

app.py

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+import gradio as gr
+
+import torch
+import librosa
+import json
+# Load model directly
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
+
+processor = AutoProcessor.from_pretrained("dmatekenya/whisper-large-v3-chichewa")
+model = AutoModelForSpeechSeq2Seq.from_pretrained("dmatekenya/whisper-large-v3-chichewa")
+
+def transcribe(audio_file_mic=None, audio_file_upload=None, language="English (eng)"):
+    if audio_file_mic:
+        audio_file = audio_file_mic
+    elif audio_file_upload:
+        audio_file = audio_file_upload
+    else:
+        return "Please upload an audio file or record one"
+
+    # Make sure audio is 16kHz
+    speech, sample_rate = librosa.load(audio_file)
+    if sample_rate != 16000:
+        speech = librosa.resample(speech, orig_sr=sample_rate, target_sr=16000)
+
+    # Keep the same model in memory and simply switch out the language adapters by calling load_adapter() for the model and set_target_lang() for the tokenizer
+    # language_code = iso_codes[language]
+    # processor.tokenizer.set_target_lang(language_code)
+    # model.load_adapter(language_code)
+
+    inputs = processor(speech, sampling_rate=16_000, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs).logits
+
+    ids = torch.argmax(outputs, dim=-1)[0]
+    transcription = processor.decode(ids)
+    return transcription
+
+
+description = ''''''
+
+iface = gr.Interface(fn=transcribe,
+                     inputs=[
+                         gr.Audio(source="microphone", type="filepath", label="Record Audio"),
+                         gr.Audio(source="upload", type="filepath", label="Upload Audio"),
+                         ],
+                     outputs=gr.Textbox(label="Transcription"),
+                     description=description
+                     )
+iface.launch()

requirements.txt

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+git+https://github.com/huggingface/transformers.git
+torch
+librosa