Create app.py

app.py

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+import gradio as gr
+import torchaudio
+from speechbrain.pretrained import EncoderClassifier, Tacotron2, HIFIGAN, ASR
+import os
+import soundfile as sf
+
+# Ensure output directory exists
+os.makedirs("output_audio", exist_ok=True)
+
+# Load models
+encoder = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb", savedir="models/encoder")
+tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="models/tacotron2")
+hifigan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="models/hifigan")
+asr = ASR.from_hparams(source="speechbrain/asr-transformer-librispeech", savedir="models/asr")
+
+def speech_to_text(input_audio):
+    sig, sr = torchaudio.load(input_audio)
+    transcription = asr.transcribe_file(input_audio)
+    return transcription
+
+def speech_to_speech(input_audio, target_text):
+    # Load and encode speaker from input audio
+    signal, fs = torchaudio.load(input_audio)
+    if fs != 16000:
+        signal = torchaudio.transforms.Resample(orig_freq=fs, new_freq=16000)(signal)
+    embedding = encoder.encode_batch(signal)
+
+    # Synthesize speech from text
+    mel_output, mel_length, alignment = tacotron2.encode_text(target_text, embedding)
+    waveform = hifigan.decode_batch(mel_output)
+
+    # Save output audio
+    output_path = "output_audio/synthesized_speech.wav"
+    sf.write(output_path, waveform.squeeze().cpu().numpy(), 22050)
+    return output_path
+
+def text_to_speech(text):
+    mel_output, mel_length, alignment = tacotron2.encode_text(text)
+    waveform = hifigan.decode_batch(mel_output)
+
+    output_path = "output_audio/text_to_speech.wav"
+    sf.write(output_path, waveform.squeeze().cpu().numpy(), 22050)
+    return output_path
+
+iface = gr.Interface(
+    fn={
+        "Speech to Text": speech_to_text,
+        "Text to Speech": text_to_speech,
+        "Speech to Speech": speech_to_speech
+    },
+    inputs={
+        "Speech to Text": gr.inputs.Audio(source="upload", type="file"),
+        "Text to Speech": gr.inputs.Textbox(label="Text"),
+        "Speech to Speech": [gr.inputs.Audio(source="upload", type="file"), gr.inputs.Textbox(label="Target Text")]
+    },
+    outputs={
+        "Speech to Text": gr.outputs.Textbox(label="Transcription"),
+        "Text to Speech": gr.outputs.Audio(type="file", label="Synthesized Speech"),
+        "Speech to Speech": gr.outputs.Audio(type="file", label="Synthesized Speech")
+    },
+    title="Speech Processing App",
+    description="Upload an audio file or enter text to perform various speech processing tasks.",
+    layout="vertical"
+)
+
+if __name__ == "__main__":
+    iface.launch()