added transcription app

app.py

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+from transformers import pipeline
+from transformers import WhisperForConditionalGeneration, WhisperProcessor, WhisperFeatureExtractor
+import gradio as gr
+import librosa
+
+# Prepare model for prediction
+MODEL_SPECS_ID = "dmatekenya/whisper-small_finetuned_sh_chich"
+MODEL_SPECS_BASE_ID = "openai/whisper-small"
+MODEL_SPECS_BASE_LAN_SW = "swahili"
+MODEL_SPECS_BASE_LAN_SH = "shona"
+FEATURE_EXTRACTOR = WhisperFeatureExtractor.from_pretrained(MODEL_SPECS_ID)
+PROCESSOR_SH = WhisperProcessor.from_pretrained(MODEL_SPECS_BASE_ID, 
+                                                language=MODEL_SPECS_BASE_LAN_SH, task="transcribe")
+MODEL = WhisperForConditionalGeneration.from_pretrained(MODEL_SPECS_ID)
+
+
+def transcribe(audio_file):
+  y, sr = librosa.load(audio_file, sr=16000)
+
+  input_features = PROCESSOR_SH(y, return_tensors="pt", sampling_rate=sr).input_features
+  generated_ids = MODEL.generate(inputs=input_features)
+
+  transcription = PROCESSOR_SH.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
+  return transcription
+
+
+def transcribe_audio(mic=None, file=None):
+    if mic is not None:
+        audio = mic
+    elif file is not None:
+        audio = file
+    else:
+        return "You must either provide a mic recording or a file"
+    transcription = transcribe(audio_file=audio)
+    return transcription
+
+title = "Transcribe Chichewa Audio"
+description = """
+<img src="https://i.ibb.co/5nQdGSs/logo.png">
+In this demo, test the first Automated Speech Recognition (ASR) model for Chichewa by transcribing your Chichewa voice notes. 
+For audio files, please upload short voice notes only (no longer than 30 sec).
+"""
+
+article = "Read more about the [ChichewaSpeech2Text](https://dmatekenya.github.io/Chichewa-Speech2Text/README.html) project \
+and make sure to sign-up for our first [voice note donation event](https://forms.gle/fHLESutofVvb2YFM9) on July 22. \
+You stand a chance to win Airtel or TNM units if you choose to participate in the raffle after the event"
+
+gr.Interface(
+    fn=transcribe_audio,
+    theme='grass',
+    title=title,
+    description=description,
+    article=article,
+    inputs=[
+        gr.Audio(source="microphone", type="filepath", optional=True),
+        gr.Audio(source="upload", type="filepath", optional=True),
+    ],
+    outputs="text",
+).launch()

requirements.txt

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+transformers
+librosa
+torch
+'transformers[torch]'